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# Titulo del proyecto
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# Module 1: Logistic Regression Classifier
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Author: Sofia Samaniego Lopez
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Institution: Universidad Autonoma de Baja California (UABC)
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Advisor: Dr. Gerardo Marx Chavez Campos
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This script establishes the baseline evaluation for binary classification using
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the classic Iris dataset. The objective of this specific work is to
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analyze linear combination, decision boundaries, and probability estimation based
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on morphological features—specifically petal width and length.
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The model utilizes Scikit-Learn's LogisticRegression framework to execute the
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optimization process and map inputs to a probability output using the Sigmoid function.
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This code serves as a strict benchmark comparison, generating the reference
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ground truth metrics and spatial visualizations required to evaluate the performance
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and accuracy of subsequent custom implementations.
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## Experimental Setup and Preliminary Analysis
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### Step 1: Import Required Libraries and Environment Setup
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In this initial stage, the necessary scientific computing and data processing libraries are imported to set up our development environment:
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* **NumPy**: Utilized for efficient multi-dimensional array operations and core matrix algebra.
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* **Matplotlib (pyplot)**: Employed to generate the spatial scatter plots and map the resulting decision boundaries.
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* **Scikit-Learn**: Specifically importing the `datasets` module to fetch the target morphological data and `LogisticRegression` to serve as our validation benchmark baseline.
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```python
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!pip3 install scikit-learn
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!pip3 install matplotlib
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!pip3 install numpy
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import matplotlib.pyplot as plt
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import numpy as np
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```
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### Step 2: Load and Explore the Iris Dataset Characteristics
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The classic **Iris Dataset** is loaded into the workspace to establish our baseline classification task.
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#### Dataset Overview and Morphological Features
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The complete dataset consists of **150 samples** from three distinct species of Iris flowers (*Iris setosa*, *Iris versicolor*, and *Iris virginica*). For each sample, four continuous geometric features are available:
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1. Sepal Length
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2. Sepal Width
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3. Petal Length
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4. Petal Width
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#### Problem Simplification & Single-Feature Evaluation
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The classification task is constrained to analyze the Sigmoid function and linear thresholds on a simpler scale:
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* **Target Binarization:** The multi-class target is converted to binary labels ($y=1$ for *Iris virginica*, $y=0$ for others) to establish a clear threshold.
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* **Single-Feature Isolation:** Instead of combining dimensions, features are evaluated **one at a time** (e.g., petal width or sepal length) to inspect their independent separation power before building multi-dimensional models.
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```python
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from sklearn import datasets
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iris=datasets.load_iris()
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print(iris.DESCR)
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```
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.. _iris_dataset:
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Iris plants dataset
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--------------------
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**Data Set Characteristics:**
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:Number of Instances: 150 (50 in each of three classes)
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:Number of Attributes: 4 numeric, predictive attributes and the class
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:Attribute Information:
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- sepal length in cm
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- sepal width in cm
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- petal length in cm
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- petal width in cm
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- class:
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- Iris-Setosa
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- Iris-Versicolour
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- Iris-Virginica
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:Summary Statistics:
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============== ==== ==== ======= ===== ====================
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Min Max Mean SD Class Correlation
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============== ==== ==== ======= ===== ====================
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sepal length: 4.3 7.9 5.84 0.83 0.7826
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sepal width: 2.0 4.4 3.05 0.43 -0.4194
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petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)
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petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)
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============== ==== ==== ======= ===== ====================
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:Missing Attribute Values: None
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:Class Distribution: 33.3% for each of 3 classes.
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:Creator: R.A. Fisher
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:Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
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:Date: July, 1988
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The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken
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from Fisher's paper. Note that it's the same as in R, but not as in the UCI
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Machine Learning Repository, which has two wrong data points.
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This is perhaps the best known database to be found in the
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pattern recognition literature. Fisher's paper is a classic in the field and
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is referenced frequently to this day. (See Duda & Hart, for example.) The
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data set contains 3 classes of 50 instances each, where each class refers to a
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type of iris plant. One class is linearly separable from the other 2; the
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latter are NOT linearly separable from each other.
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.. dropdown:: References
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- Fisher, R.A. "The use of multiple measurements in taxonomic problems"
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Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to
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Mathematical Statistics" (John Wiley, NY, 1950).
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- Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.
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(Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.
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- Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
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Structure and Classification Rule for Recognition in Partially Exposed
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Environments". IEEE Transactions on Pattern Analysis and Machine
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Intelligence, Vol. PAMI-2, No. 1, 67-71.
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- Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions
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on Information Theory, May 1972, 431-433.
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- See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II
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conceptual clustering system finds 3 classes in the data.
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- Many, many more ...
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### Step 3: Exploratory Data Analysis & Target Inspection
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Prior to model optimization, a visual and structural inspection evaluates the data distribution:
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* **Unclassified Spatial Mapping:** Plotting Sepal Length (`sl`) vs. Sepal Width (`sw`) using plain markers (`'.k'`) reveals the raw data structure. This helps verify if the morphological features naturally form distinct clusters before applying any algorithmic boundaries.
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* **Target Label Mapping (`iris.target`):** Inspecting the ground-truth array to map the multi-class numerical taxonomy:
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* `0`: *Iris setosa*
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* `1`: *Iris versicolor*
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* `2`: *Iris virginica*
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This inspection links the visual spatial groups with their mathematical labels, establishing the baseline before data binarization.
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```python
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sl = iris.data[:, 0:1]
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sw = iris.data[:, 1:2]
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plt.plot(sl,sw,'.k')
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plt.show()
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```
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```python
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iris.target
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```
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array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
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2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
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2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
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### Step 4: Theoretical Sigmoid Function & Decision Boundary
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Generating a synthetic domain from -10 to 10 to plot the standalone mathematical Sigmoid function:
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$$\sigma(t) = \frac{1}{1 + e^{-t}}$$
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This visualizes how the curve maps inputs to a probability between 0 and 1, establishing the inflection point $\sigma(0) = 0.5$ as the theoretical threshold for the decision boundary.
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```python
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t= np.linspace(-10,10,100)
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sig = 1/(1+np.exp(-t))
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plt.plot(t,sig, '.b', label=r"$\sigma$")
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plt.legend(loc='upper left', fontsize=20)
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plt.show()
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```
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## Model Training and Benchmark Evaluation
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### Model 1: Iris-Setosa Classifier based on petal width
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#### Feature Selection & Setosa Target Binarization
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The dataset is filtered and restructured to evaluate a new binary classification task:
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* **Feature Vector ($X$):** Slicing index `[:, 3:]` isolates **Petal Width** as the continuous predictor variable.
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* **Target Binarization ($y$):** The criteria `(iris.target == 0).astype(int)` shifts the positive class ($y=1$) exclusively to **Iris setosa**, mapping all other species to $0$. This creates the target array seen in the output.
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```python
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x = iris.data[:, 3:]
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y = (iris.target == 0).astype(int)
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y
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```
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array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
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#### Benchmark Model Initialization and Fitting
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This cell instantiates and trains the baseline classification model using Scikit-Learn:
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* **`LogisticRegression(solver='lbfgs', random_state=42)`**: Instantiates the model using the Limited-memory BFGS (`lbfgs`) optimization solver and locks the `random_state` to 42 to ensure reproducible weight initialization.
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* **`mylr.fit(x, y)`**: Trains the classifier on the isolated feature vector `x` and the binarized target labels `y`. This optimization process computes the optimal weight ($w$) and bias ($b$) parameters that minimize the loss function.
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```python
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from sklearn.linear_model import LogisticRegression
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mylr = LogisticRegression(solver='lbfgs', random_state=42)
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mylr.fit(x,y)
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```
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<style>.sk-global {
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/* Definition of color scheme common for light and dark mode */
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--sklearn-color-text: #000;
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--sklearn-color-text-muted: #666;
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--sklearn-color-line: gray;
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/* Definition of color scheme for unfitted estimators */
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--sklearn-color-unfitted-level-0: #fff5e6;
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--sklearn-color-unfitted-level-1: #f6e4d2;
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--sklearn-color-unfitted-level-2: #ffe0b3;
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--sklearn-color-unfitted-level-3: chocolate;
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/* Definition of color scheme for fitted estimators */
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--sklearn-color-fitted-level-0: #f0f8ff;
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--sklearn-color-fitted-level-1: #d4ebff;
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--sklearn-color-fitted-level-2: #b3dbfd;
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--sklearn-color-fitted-level-3: cornflowerblue;
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}
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.sk-global.light {
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/* Specific color for light theme */
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--sklearn-color-text-on-default-background: black;
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--sklearn-color-background: white;
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--sklearn-color-border-box: black;
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--sklearn-color-icon: #696969;
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}
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.sk-global.dark {
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--sklearn-color-text-on-default-background: white;
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--sklearn-color-background: #111;
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--sklearn-color-border-box: white;
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--sklearn-color-icon: #878787;
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}
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.sk-global {
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color: var(--sklearn-color-text);
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}
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.sk-global pre {
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padding: 0;
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}
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.sk-global input.sk-hidden--visually {
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border: 0;
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clip-path: inset(100%);
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height: 1px;
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margin: -1px;
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overflow: hidden;
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padding: 0;
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position: absolute;
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width: 1px;
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}
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.sk-global div.sk-dashed-wrapped {
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border: 1px dashed var(--sklearn-color-line);
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margin: 0 0.4em 0.5em 0.4em;
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box-sizing: border-box;
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padding-bottom: 0.4em;
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background-color: var(--sklearn-color-background);
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}
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.sk-global div.sk-container {
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/* jupyter's `normalize.less` sets `[hidden] { display: none; }`
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but bootstrap.min.css set `[hidden] { display: none !important; }`
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so we also need the `!important` here to be able to override the
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default hidden behavior on the sphinx rendered scikit-learn.org.
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See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
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display: inline-block !important;
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position: relative;
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}
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.sk-global div.sk-text-repr-fallback {
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display: none;
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}
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div.sk-parallel-item,
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div.sk-serial,
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div.sk-item {
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/* draw centered vertical line to link estimators */
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background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
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background-size: 2px 100%;
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background-repeat: no-repeat;
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background-position: center center;
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}
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/* Parallel-specific style estimator block */
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.sk-global div.sk-parallel-item::after {
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content: "";
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width: 100%;
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border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
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flex-grow: 1;
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}
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.sk-global div.sk-parallel {
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display: flex;
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align-items: stretch;
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justify-content: center;
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background-color: var(--sklearn-color-background);
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position: relative;
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}
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.sk-global div.sk-parallel-item {
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display: flex;
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flex-direction: column;
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}
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.sk-global div.sk-parallel-item:first-child::after {
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align-self: flex-end;
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width: 50%;
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}
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.sk-global div.sk-parallel-item:last-child::after {
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align-self: flex-start;
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width: 50%;
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}
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.sk-global div.sk-parallel-item:only-child::after {
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width: 0;
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}
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/* Serial-specific style estimator block */
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.sk-global div.sk-serial {
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display: flex;
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flex-direction: column;
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align-items: center;
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background-color: var(--sklearn-color-background);
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padding-right: 1em;
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padding-left: 1em;
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}
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/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
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clickable and can be expanded/collapsed.
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- Pipeline and ColumnTransformer use this feature and define the default style
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- Estimators will overwrite some part of the style using the `sk-estimator` class
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*/
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/* Pipeline and ColumnTransformer style (default) */
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.sk-global div.sk-toggleable {
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/* Default theme specific background. It is overwritten whether we have a
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specific estimator or a Pipeline/ColumnTransformer */
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background-color: var(--sklearn-color-background);
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}
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/* Toggleable label */
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.sk-global label.sk-toggleable__label {
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cursor: pointer;
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display: flex;
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width: 100%;
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margin-bottom: 0;
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padding: 0.5em;
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box-sizing: border-box;
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text-align: center;
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align-items: center;
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justify-content: center;
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gap: 0.5em;
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}
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.sk-global label.sk-toggleable__label .caption {
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font-size: 0.6rem;
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font-weight: lighter;
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color: var(--sklearn-color-text-muted);
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}
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|
.sk-global label.sk-toggleable__label-arrow:before {
|
|
|
/* Arrow on the left of the label */
|
|
|
content: "▸";
|
|
|
float: left;
|
|
|
margin-right: 0.25em;
|
|
|
color: var(--sklearn-color-icon);
|
|
|
}
|
|
|
|
|
|
.sk-global label.sk-toggleable__label-arrow:hover:before {
|
|
|
color: var(--sklearn-color-text);
|
|
|
}
|
|
|
|
|
|
/* Toggleable content - dropdown */
|
|
|
|
|
|
.sk-global div.sk-toggleable__content {
|
|
|
display: none;
|
|
|
text-align: left;
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-toggleable__content.fitted {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-toggleable__content pre {
|
|
|
margin: 0.2em;
|
|
|
border-radius: 0.25em;
|
|
|
color: var(--sklearn-color-text);
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-toggleable__content.fitted pre {
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
}
|
|
|
|
|
|
.sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {
|
|
|
/* Expand drop-down */
|
|
|
display: block;
|
|
|
width: 100%;
|
|
|
overflow: visible;
|
|
|
}
|
|
|
|
|
|
.sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
|
|
|
content: "▾";
|
|
|
}
|
|
|
|
|
|
/* Pipeline/ColumnTransformer-specific style */
|
|
|
|
|
|
.sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
|
|
color: var(--sklearn-color-text);
|
|
|
background-color: var(--sklearn-color-unfitted-level-2);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
|
|
background-color: var(--sklearn-color-fitted-level-2);
|
|
|
}
|
|
|
|
|
|
/* Estimator-specific style */
|
|
|
|
|
|
/* Colorize estimator box */
|
|
|
.sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-2);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-2);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-label label.sk-toggleable__label,
|
|
|
.sk-global div.sk-label label {
|
|
|
/* The background is the default theme color */
|
|
|
color: var(--sklearn-color-text-on-default-background);
|
|
|
}
|
|
|
|
|
|
/* On hover, darken the color of the background */
|
|
|
.sk-global div.sk-label:hover label.sk-toggleable__label {
|
|
|
color: var(--sklearn-color-text);
|
|
|
background-color: var(--sklearn-color-unfitted-level-2);
|
|
|
}
|
|
|
|
|
|
/* Label box, darken color on hover, fitted */
|
|
|
.sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
|
|
|
color: var(--sklearn-color-text);
|
|
|
background-color: var(--sklearn-color-fitted-level-2);
|
|
|
}
|
|
|
|
|
|
/* Estimator label */
|
|
|
|
|
|
.sk-global div.sk-label label {
|
|
|
font-family: monospace;
|
|
|
font-weight: bold;
|
|
|
line-height: 1.2em;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-label-container {
|
|
|
text-align: center;
|
|
|
}
|
|
|
|
|
|
/* Estimator-specific */
|
|
|
.sk-global div.sk-estimator {
|
|
|
font-family: monospace;
|
|
|
border: 1px dotted var(--sklearn-color-border-box);
|
|
|
border-radius: 0.25em;
|
|
|
box-sizing: border-box;
|
|
|
margin-bottom: 0.5em;
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-estimator.fitted {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
}
|
|
|
|
|
|
/* on hover */
|
|
|
.sk-global div.sk-estimator:hover {
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-2);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-estimator.fitted:hover {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-2);
|
|
|
}
|
|
|
|
|
|
/* Specification for estimator info (e.g. "i" and "?") */
|
|
|
|
|
|
/* Common style for "i" and "?" */
|
|
|
|
|
|
.sk-estimator-doc-link,
|
|
|
a:link.sk-estimator-doc-link,
|
|
|
a:visited.sk-estimator-doc-link {
|
|
|
float: right;
|
|
|
font-size: smaller;
|
|
|
line-height: 1em;
|
|
|
font-family: monospace;
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
border-radius: 1em;
|
|
|
height: 1em;
|
|
|
width: 1em;
|
|
|
text-decoration: none !important;
|
|
|
margin-left: 0.5em;
|
|
|
text-align: center;
|
|
|
/* unfitted */
|
|
|
border: var(--sklearn-color-unfitted-level-3) 1pt solid;
|
|
|
color: var(--sklearn-color-unfitted-level-3);
|
|
|
}
|
|
|
|
|
|
.sk-estimator-doc-link.fitted,
|
|
|
a:link.sk-estimator-doc-link.fitted,
|
|
|
a:visited.sk-estimator-doc-link.fitted {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
border: var(--sklearn-color-fitted-level-3) 1pt solid;
|
|
|
color: var(--sklearn-color-fitted-level-3);
|
|
|
}
|
|
|
|
|
|
/* On hover */
|
|
|
div.sk-estimator:hover .sk-estimator-doc-link:hover,
|
|
|
.sk-estimator-doc-link:hover,
|
|
|
div.sk-label-container:hover .sk-estimator-doc-link:hover,
|
|
|
.sk-estimator-doc-link:hover {
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-3);
|
|
|
border: var(--sklearn-color-fitted-level-0) 1pt solid;
|
|
|
color: var(--sklearn-color-unfitted-level-0);
|
|
|
text-decoration: none;
|
|
|
}
|
|
|
|
|
|
div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
|
|
|
.sk-estimator-doc-link.fitted:hover,
|
|
|
div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
|
|
|
.sk-estimator-doc-link.fitted:hover {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-3);
|
|
|
border: var(--sklearn-color-fitted-level-0) 1pt solid;
|
|
|
color: var(--sklearn-color-fitted-level-0);
|
|
|
text-decoration: none;
|
|
|
}
|
|
|
|
|
|
/* Span, style for the box shown on hovering the info icon */
|
|
|
.sk-estimator-doc-link span {
|
|
|
display: none;
|
|
|
z-index: 9999;
|
|
|
position: relative;
|
|
|
font-weight: normal;
|
|
|
right: .2ex;
|
|
|
padding: .5ex;
|
|
|
margin: .5ex;
|
|
|
width: min-content;
|
|
|
min-width: 20ex;
|
|
|
max-width: 50ex;
|
|
|
color: var(--sklearn-color-text);
|
|
|
box-shadow: 2pt 2pt 4pt #999;
|
|
|
/* unfitted */
|
|
|
background: var(--sklearn-color-unfitted-level-0);
|
|
|
border: .5pt solid var(--sklearn-color-unfitted-level-3);
|
|
|
}
|
|
|
|
|
|
.sk-estimator-doc-link.fitted span {
|
|
|
/* fitted */
|
|
|
background: var(--sklearn-color-fitted-level-0);
|
|
|
border: var(--sklearn-color-fitted-level-3);
|
|
|
}
|
|
|
|
|
|
.sk-estimator-doc-link:hover span {
|
|
|
display: block;
|
|
|
}
|
|
|
|
|
|
/* "?"-specific style due to the `<a>` HTML tag */
|
|
|
|
|
|
.sk-global a.estimator_doc_link {
|
|
|
float: right;
|
|
|
font-size: 1rem;
|
|
|
line-height: 1em;
|
|
|
font-family: monospace;
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
border-radius: 1rem;
|
|
|
height: 1rem;
|
|
|
width: 1rem;
|
|
|
text-decoration: none;
|
|
|
/* unfitted */
|
|
|
color: var(--sklearn-color-unfitted-level-1);
|
|
|
border: var(--sklearn-color-unfitted-level-1) 1pt solid;
|
|
|
}
|
|
|
|
|
|
.sk-global a.estimator_doc_link.fitted {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
border: var(--sklearn-color-fitted-level-1) 1pt solid;
|
|
|
color: var(--sklearn-color-fitted-level-1);
|
|
|
}
|
|
|
|
|
|
/* On hover */
|
|
|
.sk-global a.estimator_doc_link:hover {
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-3);
|
|
|
color: var(--sklearn-color-background);
|
|
|
text-decoration: none;
|
|
|
}
|
|
|
|
|
|
.sk-global a.estimator_doc_link.fitted:hover {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-3);
|
|
|
}
|
|
|
|
|
|
.sk-top-container.sk-global {
|
|
|
/* pydata-sphinx-theme hides overflow, so scrolling is disabled.
|
|
|
We need to set it to !important and add tabindex="0" in the HTML
|
|
|
to allow keyboard-only users to navigate the display. */
|
|
|
overflow-x: scroll !important;
|
|
|
max-width: 100%;
|
|
|
}
|
|
|
|
|
|
.estimator-table {
|
|
|
font-family: monospace;
|
|
|
}
|
|
|
|
|
|
.estimator-table summary {
|
|
|
padding: .5rem;
|
|
|
cursor: pointer;
|
|
|
}
|
|
|
|
|
|
.estimator-table summary::marker {
|
|
|
font-size: 0.7rem;
|
|
|
}
|
|
|
|
|
|
.estimator-table details[open] {
|
|
|
padding-left: 0.1rem;
|
|
|
padding-right: 0.1rem;
|
|
|
padding-bottom: 0.3rem;
|
|
|
}
|
|
|
|
|
|
.estimator-table .parameters-table {
|
|
|
margin-left: auto !important;
|
|
|
margin-right: auto !important;
|
|
|
margin-top: 0;
|
|
|
}
|
|
|
|
|
|
.estimator-table .parameters-table tr:nth-child(odd) {
|
|
|
background-color: #fff;
|
|
|
}
|
|
|
|
|
|
.estimator-table .parameters-table tr:nth-child(even) {
|
|
|
background-color: #f6f6f6;
|
|
|
}
|
|
|
|
|
|
.estimator-table .parameters-table tr:hover td {
|
|
|
background-color: #e0e0e0;
|
|
|
}
|
|
|
|
|
|
.estimator-table table :is(td, th) {
|
|
|
border: 1px solid rgba(106, 105, 104, 0.232);
|
|
|
}
|
|
|
|
|
|
/*
|
|
|
`table td`is set in notebook with right text-align.
|
|
|
We need to overwrite it.
|
|
|
*/
|
|
|
.estimator-table table td.param {
|
|
|
text-align: left;
|
|
|
position: relative;
|
|
|
padding: 0;
|
|
|
}
|
|
|
|
|
|
.user-set td {
|
|
|
color:rgb(255, 94, 0);
|
|
|
text-align: left !important;
|
|
|
}
|
|
|
|
|
|
.user-set td.value {
|
|
|
color:rgb(255, 94, 0);
|
|
|
background-color: transparent;
|
|
|
}
|
|
|
|
|
|
.default td, .estimator-table th {
|
|
|
color: black;
|
|
|
text-align: left !important;
|
|
|
}
|
|
|
|
|
|
.user-set td i,
|
|
|
.default td i {
|
|
|
color: black;
|
|
|
}
|
|
|
|
|
|
td.fitted-att-type {
|
|
|
white-space: preserve nowrap;
|
|
|
}
|
|
|
|
|
|
/*
|
|
|
Styles for parameter documentation links
|
|
|
We need styling for visited so jupyter doesn't overwrite it
|
|
|
*/
|
|
|
a.param-doc-link,
|
|
|
a.param-doc-link:link,
|
|
|
a.param-doc-link:visited {
|
|
|
text-decoration: underline dashed;
|
|
|
text-underline-offset: .3em;
|
|
|
color: inherit;
|
|
|
display: block;
|
|
|
padding: .5em;
|
|
|
}
|
|
|
|
|
|
@supports(anchor-name: --doc-link) {
|
|
|
a.param-doc-link,
|
|
|
a.param-doc-link:link,
|
|
|
a.param-doc-link:visited {
|
|
|
anchor-name: --doc-link;
|
|
|
}
|
|
|
}
|
|
|
|
|
|
/* "hack" to make the entire area of the cell containing the link clickable */
|
|
|
a.param-doc-link::before {
|
|
|
position: absolute;
|
|
|
content: "";
|
|
|
inset: 0;
|
|
|
}
|
|
|
|
|
|
.param-doc-description {
|
|
|
display: none;
|
|
|
position: absolute;
|
|
|
z-index: 9999;
|
|
|
left: 0;
|
|
|
padding: .5ex;
|
|
|
margin-left: 1.5em;
|
|
|
color: var(--sklearn-color-text);
|
|
|
box-shadow: .3em .3em .4em #999;
|
|
|
width: max-content;
|
|
|
text-align: left;
|
|
|
max-height: 10em;
|
|
|
overflow-y: auto;
|
|
|
|
|
|
/* unfitted */
|
|
|
background: var(--sklearn-color-unfitted-level-0);
|
|
|
border: thin solid var(--sklearn-color-unfitted-level-3);
|
|
|
}
|
|
|
|
|
|
@supports(position-area: center right) {
|
|
|
.param-doc-description {
|
|
|
position-area: center right;
|
|
|
position: fixed;
|
|
|
margin-left: 0;
|
|
|
}
|
|
|
}
|
|
|
|
|
|
/* Fitted state for parameter tooltips */
|
|
|
.fitted .param-doc-description {
|
|
|
/* fitted */
|
|
|
background: var(--sklearn-color-fitted-level-0);
|
|
|
border: thin solid var(--sklearn-color-fitted-level-3);
|
|
|
}
|
|
|
|
|
|
.param-doc-link:hover .param-doc-description {
|
|
|
display: block;
|
|
|
}
|
|
|
|
|
|
.copy-paste-icon {
|
|
|
background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);
|
|
|
background-repeat: no-repeat;
|
|
|
background-size: 14px 14px;
|
|
|
background-position: 0;
|
|
|
display: inline-block;
|
|
|
width: 14px;
|
|
|
height: 14px;
|
|
|
cursor: pointer;
|
|
|
}
|
|
|
|
|
|
.features {
|
|
|
font-family: monospace;
|
|
|
cursor: pointer;
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
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border: 1px dotted var(--sklearn-color-border-box);
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border-radius: .20em;
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margin-bottom: 0.5em;
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font-size: inherit; /* Needed for jupyter */
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}
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.features.fitted {
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background-color: var(--sklearn-color-fitted-level-0);
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}
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.features summary {
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cursor: pointer;
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display: flex;
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margin-bottom: 0;
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text-align: center;
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align-items: center;
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justify-content: center;
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gap: 0.5em;
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padding: .25em;
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}
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.features details[open] > summary {
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color: var(--sklearn-color-text);
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background-color: var(--sklearn-color-unfitted-level-2);
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border-radius: .20em 0 0 0;
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}
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.features.fitted details[open] > summary {
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background-color: var(--sklearn-color-fitted-level-2);
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}
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.features details > summary .arrow::before {
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content: "▸";
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color: grey;
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.features details[open] > summary .arrow::before {
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margin: 0;
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background-color: var(--sklearn-color-fitted-level-2);
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max-width: 15em;
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overflow: auto;
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background-color: var(--sklearn-color-unfitted-level-0);
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}
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.features.fitted .features-container {
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background-color: var(--sklearn-color-fitted-level-0);
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}
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display: flex;
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justify-content: center;
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align-items: center;
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}
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background-size: 1em 1em;
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width: 1em;
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height: 1em;
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filter: grayscale(100%) opacity(60%);
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}
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width: 100%;
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.features .features-container table tr:nth-child(odd) {
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background-color: #fff;
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}
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.features .features-container table tr:nth-child(even) {
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background-color: #f6f6f6;
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}
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.features .features-container table tr:hover {
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background-color: #e0e0e0;
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}
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.features .features-container table {
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table-layout: inherit;
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}
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text-align: left;
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padding: 0 0.5em;
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border: 1px solid rgba(106, 105, 104, 0.232);
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white-space: nowrap;
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color: var(--sklearn-color-text);
|
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}
|
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.total_features {
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display: flex;
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justify-content: center;
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margin-top: 0.5em;
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}
|
|
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</style><body><div id="sk-container-id-1" tabindex="0" class="sk-top-container sk-global"><div class="sk-text-repr-fallback"><pre>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually sk-global" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted" data-param-prefix="">
|
|
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<div class="estimator-table">
|
|
|
<details>
|
|
|
<summary>Parameters</summary>
|
|
|
<table class="parameters-table">
|
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|
<tbody>
|
|
|
|
|
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<tr class="user-set">
|
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<td><i class="copy-paste-icon"
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|
|
onclick="copyToClipboard('random_state',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-random_state;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone">
|
|
|
random_state
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-random_state;">
|
|
|
random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">42</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('penalty',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-penalty;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27">
|
|
|
penalty
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-penalty;">
|
|
|
penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add an L2 penalty term and it is the default choice;<br>- `'l1'`: add an L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br> Some penalties may not work with some solvers. See the parameter<br> `solver` below, to know the compatibility between the penalty and<br> solver.<br><br>.. versionadded:: 0.19<br> l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br> `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br> Use `l1_ratio` and `C` instead. `l1_ratio=0` for `penalty='l2'`,<br> `l1_ratio=1` for `penalty='l1'`, `l1_ratio` set to any float between 0 and 1<br> for `penalty='elasticnet'`, and `C=np.inf` for `penalty=None`.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">'deprecated'</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('C',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-C;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0">
|
|
|
C
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-C;">
|
|
|
C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">1.0</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('l1_ratio',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-l1_ratio;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0">
|
|
|
l1_ratio
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-l1_ratio;">
|
|
|
l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br> Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br> solvers. See the parameter `solver` below, to know the compatibility between<br> the penalty and solver.<br><br>.. versionchanged:: 1.8<br> Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br> `None` is deprecated and will be removed in version 1.10. Always use<br> `l1_ratio` to specify the penalty type.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">0.0</td>
|
|
|
</tr>
|
|
|
|
|
|
|
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|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
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|
onclick="copyToClipboard('dual',
|
|
|
this.parentElement.nextElementSibling)"
|
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|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-dual;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse">
|
|
|
dual
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-dual;">
|
|
|
dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">False</td>
|
|
|
</tr>
|
|
|
|
|
|
|
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|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
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|
onclick="copyToClipboard('tol',
|
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this.parentElement.nextElementSibling)"
|
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></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-tol;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4">
|
|
|
tol
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-tol;">
|
|
|
tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">0.0001</td>
|
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|
</tr>
|
|
|
|
|
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|
<tr class="default">
|
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|
<td><i class="copy-paste-icon"
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|
onclick="copyToClipboard('fit_intercept',
|
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|
this.parentElement.nextElementSibling)"
|
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></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-fit_intercept;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue">
|
|
|
fit_intercept
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-fit_intercept;">
|
|
|
fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">True</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('intercept_scaling',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-intercept_scaling;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1">
|
|
|
intercept_scaling
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-intercept_scaling;">
|
|
|
intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a "synthetic" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br> The synthetic feature weight is subject to L1 or L2<br> regularization as all other features.<br> To lessen the effect of regularization on synthetic feature weight<br> (and therefore on the intercept) `intercept_scaling` has to be increased.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">1</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('class_weight',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-class_weight;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone">
|
|
|
class_weight
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-class_weight;">
|
|
|
class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The "balanced" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br> *class_weight='balanced'*</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">None</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('solver',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-solver;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27">
|
|
|
solver
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-solver;">
|
|
|
solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'}, default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br> class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br> 'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br> error.<br>- 'newton-cholesky' is a good choice for<br> `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br> categorical features with rare categories. Be aware that the memory usage<br> of this solver has a quadratic dependency on `n_features * n_classes`<br> because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br> and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br> one-versus-rest scheme for the multiclass setting one can wrap it with the<br> :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br> The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br> for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br> Elastic-Net) and on (multinomial) multiclass support:<br><br> ================= ======================== ======================<br> solver l1_ratio multinomial multiclass<br> ================= ======================== ======================<br> 'lbfgs' l1_ratio=0 yes<br> 'liblinear' l1_ratio=1 or l1_ratio=0 no<br> 'newton-cg' l1_ratio=0 yes<br> 'newton-cholesky' l1_ratio=0 yes<br> 'sag' l1_ratio=0 yes<br> 'saga' 0<=l1_ratio<=1 yes<br> ================= ======================== ======================<br><br>.. note::<br> 'sag' and 'saga' fast convergence is only guaranteed on features<br> with approximately the same scale. You can preprocess the data with<br> a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br> Refer to the :ref:`User Guide <Logistic_regression>` for more<br> information regarding :class:`LogisticRegression` and more specifically the<br> :ref:`Table <logistic_regression_solvers>`<br> summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br> Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br> version 0.18.<br>.. versionadded:: 0.19<br> SAGA solver.<br>.. versionchanged:: 0.22<br> The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br> newton-cholesky solver. Multinomial support in version 1.6.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">'lbfgs'</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
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|
|
onclick="copyToClipboard('max_iter',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-max_iter;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100">
|
|
|
max_iter
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-max_iter;">
|
|
|
max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">100</td>
|
|
|
</tr>
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|
|
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|
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|
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|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
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|
|
onclick="copyToClipboard('verbose',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-verbose;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0">
|
|
|
verbose
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-verbose;">
|
|
|
verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">0</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
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<td><i class="copy-paste-icon"
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|
onclick="copyToClipboard('warm_start',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-warm_start;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse">
|
|
|
warm_start
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-warm_start;">
|
|
|
warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br> *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">False</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('n_jobs',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-n_jobs;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone">
|
|
|
n_jobs
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-n_jobs;">
|
|
|
n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br> `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">None</td>
|
|
|
</tr>
|
|
|
|
|
|
</tbody>
|
|
|
</table>
|
|
|
</details>
|
|
|
</div>
|
|
|
|
|
|
<div class="estimator-table">
|
|
|
<details>
|
|
|
<summary>Fitted attributes</summary>
|
|
|
<table class="parameters-table">
|
|
|
<tbody>
|
|
|
<tr>
|
|
|
<th>Name</th>
|
|
|
<th>Type</th>
|
|
|
<th>Value</th>
|
|
|
</tr>
|
|
|
|
|
|
<tr class="default">
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-classes_;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%20%29">
|
|
|
classes_
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-classes_;">
|
|
|
classes_: ndarray of shape (n_classes, )<br><br>A list of class labels known to the classifier.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="fitted-att-type">ndarray[int64](2,)</td>
|
|
|
<td>[0,1]</td>
|
|
|
|
|
|
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-coef_;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=coef_,-ndarray%20or%20CSR%20matrix%20of%20shape%20%281%2C%20n_features%29%20or%20%28n_classes%2C%20n_features%29">
|
|
|
coef_
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-coef_;">
|
|
|
coef_: ndarray or CSR matrix of shape (1, n_features) or (n_classes, n_features)<br><br>Coefficients of the features in the decision function.<br><br>`coef_` is of shape (1, n_features) when the given problem is binary.<br><br>By default, it will be created as a dense array, but can be turned to<br>sparse (CSR format) through :meth:`sparsify` (which can be beneficial<br>under L1 regularization when many coefficients are zero), and back to<br>dense through :meth:`densify`.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="fitted-att-type">ndarray[float64](1, 1)</td>
|
|
|
<td>[[-4.53]]</td>
|
|
|
|
|
|
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-intercept_;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20or%20%28n_classes%2C%29">
|
|
|
intercept_
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-intercept_;">
|
|
|
intercept_: ndarray of shape (1,) or (n_classes,)<br><br>Intercept (a.k.a. bias) added to the decision function.<br><br>If `fit_intercept` is set to False, the intercept is set to zero.<br>`intercept_` is of shape (1,) when the given problem is binary.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="fitted-att-type">ndarray[float64](1,)</td>
|
|
|
<td>[3.42]</td>
|
|
|
|
|
|
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-n_features_in_;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_features_in_,-int">
|
|
|
n_features_in_
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-n_features_in_;">
|
|
|
n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="fitted-att-type">int</td>
|
|
|
<td>1</td>
|
|
|
|
|
|
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-n_iter_;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_iter_,-ndarray%20of%20shape%20%281%2C%20%29">
|
|
|
n_iter_
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-n_iter_;">
|
|
|
n_iter_: ndarray of shape (1, )<br><br>Actual number of iterations for all classes.<br><br>.. versionchanged:: 0.20<br><br> In SciPy <= 1.0.0 the number of lbfgs iterations may exceed<br> ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="fitted-att-type">ndarray[int32](1,)</td>
|
|
|
<td>[9]</td>
|
|
|
|
|
|
|
|
|
</tr>
|
|
|
|
|
|
</tbody>
|
|
|
</table>
|
|
|
</details>
|
|
|
</div>
|
|
|
</div></div></div></div></div><script>/* Authors: The scikit-learn developers
|
|
|
SPDX-License-Identifier: BSD-3-Clause
|
|
|
*/
|
|
|
|
|
|
function copyToClipboard(text, element) {
|
|
|
// Get the parameter prefix from the closest toggleable content
|
|
|
const toggleableContent = element.closest('.sk-toggleable__content');
|
|
|
const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';
|
|
|
const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;
|
|
|
|
|
|
const originalStyle = element.style;
|
|
|
const computedStyle = window.getComputedStyle(element);
|
|
|
const originalWidth = computedStyle.width;
|
|
|
const originalHTML = element.innerHTML.replace('Copied!', '');
|
|
|
|
|
|
navigator.clipboard.writeText(fullParamName)
|
|
|
.then(() => {
|
|
|
element.style.width = originalWidth;
|
|
|
element.style.color = 'green';
|
|
|
element.innerHTML = "Copied!";
|
|
|
|
|
|
setTimeout(() => {
|
|
|
element.innerHTML = originalHTML;
|
|
|
element.style = originalStyle;
|
|
|
}, 2000);
|
|
|
})
|
|
|
.catch(err => {
|
|
|
console.error('Failed to copy:', err);
|
|
|
element.style.color = 'red';
|
|
|
element.innerHTML = "Failed!";
|
|
|
setTimeout(() => {
|
|
|
element.innerHTML = originalHTML;
|
|
|
element.style = originalStyle;
|
|
|
}, 2000);
|
|
|
});
|
|
|
return false;
|
|
|
}
|
|
|
|
|
|
document.querySelectorAll('.copy-paste-icon').forEach(function(element) {
|
|
|
const toggleableContent = element.closest('.sk-toggleable__content');
|
|
|
const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';
|
|
|
|
|
|
const parent = element.parentElement;
|
|
|
if (!parent || !parent.nextElementSibling) {
|
|
|
console.warn('Expected copy-paste icon is missing from the DOM structure');
|
|
|
return;
|
|
|
}
|
|
|
|
|
|
const paramName = element.parentElement.nextElementSibling
|
|
|
.textContent.trim().split(' ')[0];
|
|
|
const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;
|
|
|
|
|
|
element.setAttribute('title', fullParamName);
|
|
|
});
|
|
|
|
|
|
/**
|
|
|
* Copy the list of feature names formatted as a Python list.
|
|
|
*
|
|
|
* @param {HTMLElement} element - The copy button inside a `.features` block; its siblings
|
|
|
* contain a `details` element and a table containing feature named.
|
|
|
* @returns {boolean} Always returns `false` so callers can prevent the default click behavior.
|
|
|
*/
|
|
|
function copyFeatureNamesToClipboard(element) {
|
|
|
var detailsElem = element.closest('.features').querySelector('details');
|
|
|
var wasOpen = detailsElem.open;
|
|
|
detailsElem.open = true;
|
|
|
var content = element.closest('.features').querySelector('tbody')
|
|
|
.innerText.trim();
|
|
|
if (!wasOpen) detailsElem.open = false;
|
|
|
const rows = content.split('\n').map(row => ` "${row}"`);
|
|
|
const formattedText = `[\n${rows.join(',\n')},\n]`;
|
|
|
const originalHTML = element.innerHTML.replace('✔', '');
|
|
|
const originalStyle = element.style;
|
|
|
const copyMark = document.createElement('span');
|
|
|
copyMark.innerHTML = '✔';
|
|
|
copyMark.style.color = 'blue';
|
|
|
copyMark.style.fontSize = '1em';
|
|
|
|
|
|
navigator.clipboard.writeText(formattedText)
|
|
|
.then(() => {
|
|
|
element.style.display = 'none';
|
|
|
element.parentElement.appendChild(copyMark);
|
|
|
|
|
|
setTimeout(() => {
|
|
|
copyMark.remove();
|
|
|
element.innerHTML = originalHTML;
|
|
|
element.style = originalStyle;
|
|
|
}, 1000);
|
|
|
})
|
|
|
.catch(err => {
|
|
|
console.error('Failed to copy:', err);
|
|
|
element.style.color = 'orange';
|
|
|
element.innerHTML = "Failed!";
|
|
|
setTimeout(() => {
|
|
|
element.innerHTML = originalHTML;
|
|
|
element.style = originalStyle;
|
|
|
}, 1000);
|
|
|
});
|
|
|
return false;
|
|
|
}
|
|
|
/**
|
|
|
* Adapted from Skrub
|
|
|
* https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789
|
|
|
* @returns "light" or "dark"
|
|
|
*/
|
|
|
function detectTheme(element) {
|
|
|
const body = document.querySelector('body');
|
|
|
|
|
|
// Check VSCode theme
|
|
|
const themeKindAttr = body.getAttribute('data-vscode-theme-kind');
|
|
|
const themeNameAttr = body.getAttribute('data-vscode-theme-name');
|
|
|
|
|
|
if (themeKindAttr && themeNameAttr) {
|
|
|
const themeKind = themeKindAttr.toLowerCase();
|
|
|
const themeName = themeNameAttr.toLowerCase();
|
|
|
|
|
|
if (themeKind.includes("dark") || themeName.includes("dark")) {
|
|
|
return "dark";
|
|
|
}
|
|
|
if (themeKind.includes("light") || themeName.includes("light")) {
|
|
|
return "light";
|
|
|
}
|
|
|
}
|
|
|
|
|
|
// Check Jupyter theme
|
|
|
if (body.getAttribute('data-jp-theme-light') === 'false') {
|
|
|
return 'dark';
|
|
|
} else if (body.getAttribute('data-jp-theme-light') === 'true') {
|
|
|
return 'light';
|
|
|
}
|
|
|
|
|
|
// Guess based on a parent element's color
|
|
|
const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');
|
|
|
const match = color.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)\s*$/i);
|
|
|
if (match) {
|
|
|
const [r, g, b] = [
|
|
|
parseFloat(match[1]),
|
|
|
parseFloat(match[2]),
|
|
|
parseFloat(match[3])
|
|
|
];
|
|
|
|
|
|
// https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness
|
|
|
const luma = 0.299 * r + 0.587 * g + 0.114 * b;
|
|
|
|
|
|
if (luma > 180) {
|
|
|
// If the text is very bright we have a dark theme
|
|
|
return 'dark';
|
|
|
}
|
|
|
if (luma < 75) {
|
|
|
// If the text is very dark we have a light theme
|
|
|
return 'light';
|
|
|
}
|
|
|
// Otherwise fall back to the next heuristic.
|
|
|
}
|
|
|
|
|
|
// Fallback to system preference
|
|
|
return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';
|
|
|
}
|
|
|
|
|
|
|
|
|
function forceTheme(elementId) {
|
|
|
const estimatorElement = document.querySelector(`#${elementId}`);
|
|
|
if (estimatorElement === null) {
|
|
|
console.error(`Element with id ${elementId} not found.`);
|
|
|
} else {
|
|
|
const theme = detectTheme(estimatorElement);
|
|
|
estimatorElement.classList.add(theme);
|
|
|
}
|
|
|
}
|
|
|
|
|
|
forceTheme('sk-container-id-1');</script></body>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
```python
|
|
|
Xnew = np.linspace(-1,3,100).reshape(-1,1)
|
|
|
yPred = mylr.predict_proba(Xnew)
|
|
|
#plt.plot(Xnew, yPred[:,0], label= 'No Iris')
|
|
|
plt.plot(Xnew, yPred[:,1], label= 'Yes Iris')
|
|
|
plt.legend()
|
|
|
plt.plot(x,y,'*g')
|
|
|
plt.show()
|
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
This plot visualizes the trained model's Sigmoid prediction curve over the experimental dataset samples:
|
|
|
|
|
|
* **Sample Distribution (Green Stars):** Represents the real dataset. Small petal widths (0.1 - 0.6 cm) belong to *Iris setosa* ($y=1$), while larger widths (1.0 - 2.5 cm) belong to the other species ($y=0$).
|
|
|
* **Sigmoid Mapping (Blue Curve):** Displays an inverted logistic curve. It demonstrates the mathematical relationship: as petal width increases, the probability of the flower being *Iris setosa* drops sharply from 1.0 to 0.0.
|
|
|
* **Decision Boundary Threshold:** The curve crosses the 0.5 probability threshold at approximately 0.75 cm. This inflection point defines the exact baseline boundary separating both classifications.
|
|
|
|
|
|
### Model 2: Iris-Setosa Classifier based on petal length
|
|
|
|
|
|
#### Feature Shift – Petal Length Isolation
|
|
|
|
|
|
The model configuration is updated to evaluate a different morphological predictor:
|
|
|
* **Feature Vector ($X$):** Slicing index `[:, 2:3]` isolates **Petal Length** as the independent variable.
|
|
|
* **Target Continuity ($y$):** The classification objective remains focused on **Iris setosa** (`iris.target == 0`) to compare the separation power of petal length against the previous petal width baseline.
|
|
|
|
|
|
|
|
|
```python
|
|
|
x = iris.data[:, 2:3]
|
|
|
y = (iris.target == 0).astype(int)
|
|
|
|
|
|
```
|
|
|
|
|
|
|
|
|
```python
|
|
|
from sklearn.linear_model import LogisticRegression
|
|
|
mylr = LogisticRegression(solver='lbfgs', random_state=42)
|
|
|
mylr.fit(x,y)
|
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
<style>.sk-global {
|
|
|
/* Definition of color scheme common for light and dark mode */
|
|
|
--sklearn-color-text: #000;
|
|
|
--sklearn-color-text-muted: #666;
|
|
|
--sklearn-color-line: gray;
|
|
|
/* Definition of color scheme for unfitted estimators */
|
|
|
--sklearn-color-unfitted-level-0: #fff5e6;
|
|
|
--sklearn-color-unfitted-level-1: #f6e4d2;
|
|
|
--sklearn-color-unfitted-level-2: #ffe0b3;
|
|
|
--sklearn-color-unfitted-level-3: chocolate;
|
|
|
/* Definition of color scheme for fitted estimators */
|
|
|
--sklearn-color-fitted-level-0: #f0f8ff;
|
|
|
--sklearn-color-fitted-level-1: #d4ebff;
|
|
|
--sklearn-color-fitted-level-2: #b3dbfd;
|
|
|
--sklearn-color-fitted-level-3: cornflowerblue;
|
|
|
}
|
|
|
|
|
|
.sk-global.light {
|
|
|
/* Specific color for light theme */
|
|
|
--sklearn-color-text-on-default-background: black;
|
|
|
--sklearn-color-background: white;
|
|
|
--sklearn-color-border-box: black;
|
|
|
--sklearn-color-icon: #696969;
|
|
|
}
|
|
|
|
|
|
.sk-global.dark {
|
|
|
--sklearn-color-text-on-default-background: white;
|
|
|
--sklearn-color-background: #111;
|
|
|
--sklearn-color-border-box: white;
|
|
|
--sklearn-color-icon: #878787;
|
|
|
}
|
|
|
|
|
|
.sk-global {
|
|
|
color: var(--sklearn-color-text);
|
|
|
}
|
|
|
|
|
|
.sk-global pre {
|
|
|
padding: 0;
|
|
|
}
|
|
|
|
|
|
.sk-global input.sk-hidden--visually {
|
|
|
border: 0;
|
|
|
clip-path: inset(100%);
|
|
|
height: 1px;
|
|
|
margin: -1px;
|
|
|
overflow: hidden;
|
|
|
padding: 0;
|
|
|
position: absolute;
|
|
|
width: 1px;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-dashed-wrapped {
|
|
|
border: 1px dashed var(--sklearn-color-line);
|
|
|
margin: 0 0.4em 0.5em 0.4em;
|
|
|
box-sizing: border-box;
|
|
|
padding-bottom: 0.4em;
|
|
|
background-color: var(--sklearn-color-background);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-container {
|
|
|
/* jupyter's `normalize.less` sets `[hidden] { display: none; }`
|
|
|
but bootstrap.min.css set `[hidden] { display: none !important; }`
|
|
|
so we also need the `!important` here to be able to override the
|
|
|
default hidden behavior on the sphinx rendered scikit-learn.org.
|
|
|
See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
|
|
|
display: inline-block !important;
|
|
|
position: relative;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-text-repr-fallback {
|
|
|
display: none;
|
|
|
}
|
|
|
|
|
|
div.sk-parallel-item,
|
|
|
div.sk-serial,
|
|
|
div.sk-item {
|
|
|
/* draw centered vertical line to link estimators */
|
|
|
background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
|
|
|
background-size: 2px 100%;
|
|
|
background-repeat: no-repeat;
|
|
|
background-position: center center;
|
|
|
}
|
|
|
|
|
|
/* Parallel-specific style estimator block */
|
|
|
|
|
|
.sk-global div.sk-parallel-item::after {
|
|
|
content: "";
|
|
|
width: 100%;
|
|
|
border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
|
|
|
flex-grow: 1;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-parallel {
|
|
|
display: flex;
|
|
|
align-items: stretch;
|
|
|
justify-content: center;
|
|
|
background-color: var(--sklearn-color-background);
|
|
|
position: relative;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-parallel-item {
|
|
|
display: flex;
|
|
|
flex-direction: column;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-parallel-item:first-child::after {
|
|
|
align-self: flex-end;
|
|
|
width: 50%;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-parallel-item:last-child::after {
|
|
|
align-self: flex-start;
|
|
|
width: 50%;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-parallel-item:only-child::after {
|
|
|
width: 0;
|
|
|
}
|
|
|
|
|
|
/* Serial-specific style estimator block */
|
|
|
|
|
|
.sk-global div.sk-serial {
|
|
|
display: flex;
|
|
|
flex-direction: column;
|
|
|
align-items: center;
|
|
|
background-color: var(--sklearn-color-background);
|
|
|
padding-right: 1em;
|
|
|
padding-left: 1em;
|
|
|
}
|
|
|
|
|
|
|
|
|
/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
|
|
|
clickable and can be expanded/collapsed.
|
|
|
- Pipeline and ColumnTransformer use this feature and define the default style
|
|
|
- Estimators will overwrite some part of the style using the `sk-estimator` class
|
|
|
*/
|
|
|
|
|
|
/* Pipeline and ColumnTransformer style (default) */
|
|
|
|
|
|
.sk-global div.sk-toggleable {
|
|
|
/* Default theme specific background. It is overwritten whether we have a
|
|
|
specific estimator or a Pipeline/ColumnTransformer */
|
|
|
background-color: var(--sklearn-color-background);
|
|
|
}
|
|
|
|
|
|
/* Toggleable label */
|
|
|
.sk-global label.sk-toggleable__label {
|
|
|
cursor: pointer;
|
|
|
display: flex;
|
|
|
width: 100%;
|
|
|
margin-bottom: 0;
|
|
|
padding: 0.5em;
|
|
|
box-sizing: border-box;
|
|
|
text-align: center;
|
|
|
align-items: center;
|
|
|
justify-content: center;
|
|
|
gap: 0.5em;
|
|
|
}
|
|
|
|
|
|
.sk-global label.sk-toggleable__label .caption {
|
|
|
font-size: 0.6rem;
|
|
|
font-weight: lighter;
|
|
|
color: var(--sklearn-color-text-muted);
|
|
|
}
|
|
|
|
|
|
.sk-global label.sk-toggleable__label-arrow:before {
|
|
|
/* Arrow on the left of the label */
|
|
|
content: "▸";
|
|
|
float: left;
|
|
|
margin-right: 0.25em;
|
|
|
color: var(--sklearn-color-icon);
|
|
|
}
|
|
|
|
|
|
.sk-global label.sk-toggleable__label-arrow:hover:before {
|
|
|
color: var(--sklearn-color-text);
|
|
|
}
|
|
|
|
|
|
/* Toggleable content - dropdown */
|
|
|
|
|
|
.sk-global div.sk-toggleable__content {
|
|
|
display: none;
|
|
|
text-align: left;
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-toggleable__content.fitted {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-toggleable__content pre {
|
|
|
margin: 0.2em;
|
|
|
border-radius: 0.25em;
|
|
|
color: var(--sklearn-color-text);
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-toggleable__content.fitted pre {
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
}
|
|
|
|
|
|
.sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {
|
|
|
/* Expand drop-down */
|
|
|
display: block;
|
|
|
width: 100%;
|
|
|
overflow: visible;
|
|
|
}
|
|
|
|
|
|
.sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
|
|
|
content: "▾";
|
|
|
}
|
|
|
|
|
|
/* Pipeline/ColumnTransformer-specific style */
|
|
|
|
|
|
.sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
|
|
color: var(--sklearn-color-text);
|
|
|
background-color: var(--sklearn-color-unfitted-level-2);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
|
|
background-color: var(--sklearn-color-fitted-level-2);
|
|
|
}
|
|
|
|
|
|
/* Estimator-specific style */
|
|
|
|
|
|
/* Colorize estimator box */
|
|
|
.sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-2);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-2);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-label label.sk-toggleable__label,
|
|
|
.sk-global div.sk-label label {
|
|
|
/* The background is the default theme color */
|
|
|
color: var(--sklearn-color-text-on-default-background);
|
|
|
}
|
|
|
|
|
|
/* On hover, darken the color of the background */
|
|
|
.sk-global div.sk-label:hover label.sk-toggleable__label {
|
|
|
color: var(--sklearn-color-text);
|
|
|
background-color: var(--sklearn-color-unfitted-level-2);
|
|
|
}
|
|
|
|
|
|
/* Label box, darken color on hover, fitted */
|
|
|
.sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
|
|
|
color: var(--sklearn-color-text);
|
|
|
background-color: var(--sklearn-color-fitted-level-2);
|
|
|
}
|
|
|
|
|
|
/* Estimator label */
|
|
|
|
|
|
.sk-global div.sk-label label {
|
|
|
font-family: monospace;
|
|
|
font-weight: bold;
|
|
|
line-height: 1.2em;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-label-container {
|
|
|
text-align: center;
|
|
|
}
|
|
|
|
|
|
/* Estimator-specific */
|
|
|
.sk-global div.sk-estimator {
|
|
|
font-family: monospace;
|
|
|
border: 1px dotted var(--sklearn-color-border-box);
|
|
|
border-radius: 0.25em;
|
|
|
box-sizing: border-box;
|
|
|
margin-bottom: 0.5em;
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-estimator.fitted {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
}
|
|
|
|
|
|
/* on hover */
|
|
|
.sk-global div.sk-estimator:hover {
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-2);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-estimator.fitted:hover {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-2);
|
|
|
}
|
|
|
|
|
|
/* Specification for estimator info (e.g. "i" and "?") */
|
|
|
|
|
|
/* Common style for "i" and "?" */
|
|
|
|
|
|
.sk-estimator-doc-link,
|
|
|
a:link.sk-estimator-doc-link,
|
|
|
a:visited.sk-estimator-doc-link {
|
|
|
float: right;
|
|
|
font-size: smaller;
|
|
|
line-height: 1em;
|
|
|
font-family: monospace;
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
border-radius: 1em;
|
|
|
height: 1em;
|
|
|
width: 1em;
|
|
|
text-decoration: none !important;
|
|
|
margin-left: 0.5em;
|
|
|
text-align: center;
|
|
|
/* unfitted */
|
|
|
border: var(--sklearn-color-unfitted-level-3) 1pt solid;
|
|
|
color: var(--sklearn-color-unfitted-level-3);
|
|
|
}
|
|
|
|
|
|
.sk-estimator-doc-link.fitted,
|
|
|
a:link.sk-estimator-doc-link.fitted,
|
|
|
a:visited.sk-estimator-doc-link.fitted {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
border: var(--sklearn-color-fitted-level-3) 1pt solid;
|
|
|
color: var(--sklearn-color-fitted-level-3);
|
|
|
}
|
|
|
|
|
|
/* On hover */
|
|
|
div.sk-estimator:hover .sk-estimator-doc-link:hover,
|
|
|
.sk-estimator-doc-link:hover,
|
|
|
div.sk-label-container:hover .sk-estimator-doc-link:hover,
|
|
|
.sk-estimator-doc-link:hover {
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-3);
|
|
|
border: var(--sklearn-color-fitted-level-0) 1pt solid;
|
|
|
color: var(--sklearn-color-unfitted-level-0);
|
|
|
text-decoration: none;
|
|
|
}
|
|
|
|
|
|
div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
|
|
|
.sk-estimator-doc-link.fitted:hover,
|
|
|
div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
|
|
|
.sk-estimator-doc-link.fitted:hover {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-3);
|
|
|
border: var(--sklearn-color-fitted-level-0) 1pt solid;
|
|
|
color: var(--sklearn-color-fitted-level-0);
|
|
|
text-decoration: none;
|
|
|
}
|
|
|
|
|
|
/* Span, style for the box shown on hovering the info icon */
|
|
|
.sk-estimator-doc-link span {
|
|
|
display: none;
|
|
|
z-index: 9999;
|
|
|
position: relative;
|
|
|
font-weight: normal;
|
|
|
right: .2ex;
|
|
|
padding: .5ex;
|
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margin: .5ex;
|
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width: min-content;
|
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min-width: 20ex;
|
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max-width: 50ex;
|
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color: var(--sklearn-color-text);
|
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|
box-shadow: 2pt 2pt 4pt #999;
|
|
|
/* unfitted */
|
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|
background: var(--sklearn-color-unfitted-level-0);
|
|
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border: .5pt solid var(--sklearn-color-unfitted-level-3);
|
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|
}
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.sk-estimator-doc-link.fitted span {
|
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/* fitted */
|
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background: var(--sklearn-color-fitted-level-0);
|
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border: var(--sklearn-color-fitted-level-3);
|
|
|
}
|
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|
.sk-estimator-doc-link:hover span {
|
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display: block;
|
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}
|
|
|
|
|
|
/* "?"-specific style due to the `<a>` HTML tag */
|
|
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|
.sk-global a.estimator_doc_link {
|
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|
float: right;
|
|
|
font-size: 1rem;
|
|
|
line-height: 1em;
|
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font-family: monospace;
|
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|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
border-radius: 1rem;
|
|
|
height: 1rem;
|
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|
width: 1rem;
|
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text-decoration: none;
|
|
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/* unfitted */
|
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|
color: var(--sklearn-color-unfitted-level-1);
|
|
|
border: var(--sklearn-color-unfitted-level-1) 1pt solid;
|
|
|
}
|
|
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|
.sk-global a.estimator_doc_link.fitted {
|
|
|
/* fitted */
|
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|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
border: var(--sklearn-color-fitted-level-1) 1pt solid;
|
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color: var(--sklearn-color-fitted-level-1);
|
|
|
}
|
|
|
|
|
|
/* On hover */
|
|
|
.sk-global a.estimator_doc_link:hover {
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-3);
|
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color: var(--sklearn-color-background);
|
|
|
text-decoration: none;
|
|
|
}
|
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|
.sk-global a.estimator_doc_link.fitted:hover {
|
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/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-3);
|
|
|
}
|
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|
.sk-top-container.sk-global {
|
|
|
/* pydata-sphinx-theme hides overflow, so scrolling is disabled.
|
|
|
We need to set it to !important and add tabindex="0" in the HTML
|
|
|
to allow keyboard-only users to navigate the display. */
|
|
|
overflow-x: scroll !important;
|
|
|
max-width: 100%;
|
|
|
}
|
|
|
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|
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.estimator-table {
|
|
|
font-family: monospace;
|
|
|
}
|
|
|
|
|
|
.estimator-table summary {
|
|
|
padding: .5rem;
|
|
|
cursor: pointer;
|
|
|
}
|
|
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|
|
.estimator-table summary::marker {
|
|
|
font-size: 0.7rem;
|
|
|
}
|
|
|
|
|
|
.estimator-table details[open] {
|
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|
padding-left: 0.1rem;
|
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|
padding-right: 0.1rem;
|
|
|
padding-bottom: 0.3rem;
|
|
|
}
|
|
|
|
|
|
.estimator-table .parameters-table {
|
|
|
margin-left: auto !important;
|
|
|
margin-right: auto !important;
|
|
|
margin-top: 0;
|
|
|
}
|
|
|
|
|
|
.estimator-table .parameters-table tr:nth-child(odd) {
|
|
|
background-color: #fff;
|
|
|
}
|
|
|
|
|
|
.estimator-table .parameters-table tr:nth-child(even) {
|
|
|
background-color: #f6f6f6;
|
|
|
}
|
|
|
|
|
|
.estimator-table .parameters-table tr:hover td {
|
|
|
background-color: #e0e0e0;
|
|
|
}
|
|
|
|
|
|
.estimator-table table :is(td, th) {
|
|
|
border: 1px solid rgba(106, 105, 104, 0.232);
|
|
|
}
|
|
|
|
|
|
/*
|
|
|
`table td`is set in notebook with right text-align.
|
|
|
We need to overwrite it.
|
|
|
*/
|
|
|
.estimator-table table td.param {
|
|
|
text-align: left;
|
|
|
position: relative;
|
|
|
padding: 0;
|
|
|
}
|
|
|
|
|
|
.user-set td {
|
|
|
color:rgb(255, 94, 0);
|
|
|
text-align: left !important;
|
|
|
}
|
|
|
|
|
|
.user-set td.value {
|
|
|
color:rgb(255, 94, 0);
|
|
|
background-color: transparent;
|
|
|
}
|
|
|
|
|
|
.default td, .estimator-table th {
|
|
|
color: black;
|
|
|
text-align: left !important;
|
|
|
}
|
|
|
|
|
|
.user-set td i,
|
|
|
.default td i {
|
|
|
color: black;
|
|
|
}
|
|
|
|
|
|
td.fitted-att-type {
|
|
|
white-space: preserve nowrap;
|
|
|
}
|
|
|
|
|
|
/*
|
|
|
Styles for parameter documentation links
|
|
|
We need styling for visited so jupyter doesn't overwrite it
|
|
|
*/
|
|
|
a.param-doc-link,
|
|
|
a.param-doc-link:link,
|
|
|
a.param-doc-link:visited {
|
|
|
text-decoration: underline dashed;
|
|
|
text-underline-offset: .3em;
|
|
|
color: inherit;
|
|
|
display: block;
|
|
|
padding: .5em;
|
|
|
}
|
|
|
|
|
|
@supports(anchor-name: --doc-link) {
|
|
|
a.param-doc-link,
|
|
|
a.param-doc-link:link,
|
|
|
a.param-doc-link:visited {
|
|
|
anchor-name: --doc-link;
|
|
|
}
|
|
|
}
|
|
|
|
|
|
/* "hack" to make the entire area of the cell containing the link clickable */
|
|
|
a.param-doc-link::before {
|
|
|
position: absolute;
|
|
|
content: "";
|
|
|
inset: 0;
|
|
|
}
|
|
|
|
|
|
.param-doc-description {
|
|
|
display: none;
|
|
|
position: absolute;
|
|
|
z-index: 9999;
|
|
|
left: 0;
|
|
|
padding: .5ex;
|
|
|
margin-left: 1.5em;
|
|
|
color: var(--sklearn-color-text);
|
|
|
box-shadow: .3em .3em .4em #999;
|
|
|
width: max-content;
|
|
|
text-align: left;
|
|
|
max-height: 10em;
|
|
|
overflow-y: auto;
|
|
|
|
|
|
/* unfitted */
|
|
|
background: var(--sklearn-color-unfitted-level-0);
|
|
|
border: thin solid var(--sklearn-color-unfitted-level-3);
|
|
|
}
|
|
|
|
|
|
@supports(position-area: center right) {
|
|
|
.param-doc-description {
|
|
|
position-area: center right;
|
|
|
position: fixed;
|
|
|
margin-left: 0;
|
|
|
}
|
|
|
}
|
|
|
|
|
|
/* Fitted state for parameter tooltips */
|
|
|
.fitted .param-doc-description {
|
|
|
/* fitted */
|
|
|
background: var(--sklearn-color-fitted-level-0);
|
|
|
border: thin solid var(--sklearn-color-fitted-level-3);
|
|
|
}
|
|
|
|
|
|
.param-doc-link:hover .param-doc-description {
|
|
|
display: block;
|
|
|
}
|
|
|
|
|
|
.copy-paste-icon {
|
|
|
background-image: url(data:image/svg+xml;base64,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);
|
|
|
background-repeat: no-repeat;
|
|
|
background-size: 14px 14px;
|
|
|
background-position: 0;
|
|
|
display: inline-block;
|
|
|
width: 14px;
|
|
|
height: 14px;
|
|
|
cursor: pointer;
|
|
|
}
|
|
|
|
|
|
.features {
|
|
|
font-family: monospace;
|
|
|
cursor: pointer;
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
border: 1px dotted var(--sklearn-color-border-box);
|
|
|
border-radius: .20em;
|
|
|
margin-bottom: 0.5em;
|
|
|
font-size: inherit; /* Needed for jupyter */
|
|
|
}
|
|
|
|
|
|
.features.fitted {
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
}
|
|
|
|
|
|
.features summary {
|
|
|
cursor: pointer;
|
|
|
display: flex;
|
|
|
margin-bottom: 0;
|
|
|
text-align: center;
|
|
|
align-items: center;
|
|
|
justify-content: center;
|
|
|
gap: 0.5em;
|
|
|
padding: .25em;
|
|
|
}
|
|
|
|
|
|
.features details[open] > summary {
|
|
|
color: var(--sklearn-color-text);
|
|
|
background-color: var(--sklearn-color-unfitted-level-2);
|
|
|
border-radius: .20em 0 0 0;
|
|
|
}
|
|
|
|
|
|
.features.fitted details[open] > summary {
|
|
|
background-color: var(--sklearn-color-fitted-level-2);
|
|
|
border-radius: .20em 0 0 0;
|
|
|
}
|
|
|
|
|
|
.features details > summary .arrow::before {
|
|
|
content: "▸";
|
|
|
color: grey;
|
|
|
}
|
|
|
|
|
|
.features details[open] > summary .arrow::before {
|
|
|
content: "▾";
|
|
|
}
|
|
|
|
|
|
.features details:hover > summary {
|
|
|
margin: 0;
|
|
|
background-color: var(--sklearn-color-unfitted-level-2);
|
|
|
}
|
|
|
|
|
|
.features.fitted details:hover > summary {
|
|
|
margin: 0;
|
|
|
background-color: var(--sklearn-color-fitted-level-2);
|
|
|
}
|
|
|
|
|
|
.features .features-container {
|
|
|
max-width: 15em;
|
|
|
max-height: 10em;
|
|
|
overflow: auto;
|
|
|
scrollbar-width: thin;
|
|
|
padding: .25em 0.1rem;
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
border-radius: 0 0 .5em .5em;
|
|
|
}
|
|
|
|
|
|
.features.fitted .features-container {
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
}
|
|
|
|
|
|
.features .image-container {
|
|
|
block-size: 1em;
|
|
|
inline-size: 1em;
|
|
|
padding: 0;
|
|
|
margin: 0%;
|
|
|
display: flex;
|
|
|
justify-content: center;
|
|
|
align-items: center;
|
|
|
}
|
|
|
|
|
|
.features .copy-paste-icon {
|
|
|
background-size: 1em 1em;
|
|
|
width: 1em;
|
|
|
height: 1em;
|
|
|
filter: grayscale(100%) opacity(60%);
|
|
|
}
|
|
|
|
|
|
.features .features-container table {
|
|
|
width: 100%;
|
|
|
margin: 0.01em;
|
|
|
}
|
|
|
|
|
|
.features .features-container table tr:nth-child(odd) {
|
|
|
background-color: #fff;
|
|
|
}
|
|
|
|
|
|
.features .features-container table tr:nth-child(even) {
|
|
|
background-color: #f6f6f6;
|
|
|
}
|
|
|
|
|
|
.features .features-container table tr:hover {
|
|
|
background-color: #e0e0e0;
|
|
|
}
|
|
|
|
|
|
.features .features-container table {
|
|
|
table-layout: inherit;
|
|
|
}
|
|
|
|
|
|
.features .features-container table td {
|
|
|
text-align: left;
|
|
|
padding: 0 0.5em;
|
|
|
border: 1px solid rgba(106, 105, 104, 0.232);
|
|
|
white-space: nowrap;
|
|
|
color: var(--sklearn-color-text);
|
|
|
}
|
|
|
|
|
|
.total_features {
|
|
|
display: flex;
|
|
|
justify-content: center;
|
|
|
margin-top: 0.5em;
|
|
|
}
|
|
|
</style><body><div id="sk-container-id-2" tabindex="0" class="sk-top-container sk-global"><div class="sk-text-repr-fallback"><pre>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually sk-global" id="sk-estimator-id-2" type="checkbox" checked><label for="sk-estimator-id-2" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted" data-param-prefix="">
|
|
|
<div class="estimator-table">
|
|
|
<details>
|
|
|
<summary>Parameters</summary>
|
|
|
<table class="parameters-table">
|
|
|
<tbody>
|
|
|
|
|
|
<tr class="user-set">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('random_state',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-random_state;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone">
|
|
|
random_state
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-random_state;">
|
|
|
random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">42</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('penalty',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-penalty;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27">
|
|
|
penalty
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-penalty;">
|
|
|
penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add an L2 penalty term and it is the default choice;<br>- `'l1'`: add an L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br> Some penalties may not work with some solvers. See the parameter<br> `solver` below, to know the compatibility between the penalty and<br> solver.<br><br>.. versionadded:: 0.19<br> l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br> `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br> Use `l1_ratio` and `C` instead. `l1_ratio=0` for `penalty='l2'`,<br> `l1_ratio=1` for `penalty='l1'`, `l1_ratio` set to any float between 0 and 1<br> for `penalty='elasticnet'`, and `C=np.inf` for `penalty=None`.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">'deprecated'</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('C',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-C;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0">
|
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C
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-C;">
|
|
|
C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>
|
|
|
</a>
|
|
|
</td>
|
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|
<td class="value">1.0</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
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onclick="copyToClipboard('l1_ratio',
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|
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this.parentElement.nextElementSibling)"
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|
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></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
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|
|
style="anchor-name: --doc-link-l1_ratio;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0">
|
|
|
l1_ratio
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-l1_ratio;">
|
|
|
l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br> Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br> solvers. See the parameter `solver` below, to know the compatibility between<br> the penalty and solver.<br><br>.. versionchanged:: 1.8<br> Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br> `None` is deprecated and will be removed in version 1.10. Always use<br> `l1_ratio` to specify the penalty type.</span>
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|
</a>
|
|
|
</td>
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|
<td class="value">0.0</td>
|
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</tr>
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|
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|
<tr class="default">
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|
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<td><i class="copy-paste-icon"
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onclick="copyToClipboard('dual',
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this.parentElement.nextElementSibling)"
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></i></td>
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<td class="param">
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|
<a class="param-doc-link"
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|
|
style="anchor-name: --doc-link-dual;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse">
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dual
|
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|
<span class="param-doc-description"
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|
|
style="position-anchor: --doc-link-dual;">
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|
|
dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>
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</a>
|
|
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</td>
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<td class="value">False</td>
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</tr>
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<tr class="default">
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<td><i class="copy-paste-icon"
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onclick="copyToClipboard('tol',
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this.parentElement.nextElementSibling)"
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|
></i></td>
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|
<td class="param">
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|
|
<a class="param-doc-link"
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|
|
style="anchor-name: --doc-link-tol;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4">
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tol
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|
<span class="param-doc-description"
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|
style="position-anchor: --doc-link-tol;">
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|
|
tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>
|
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</a>
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</td>
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|
|
<td class="value">0.0001</td>
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</tr>
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<tr class="default">
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<td><i class="copy-paste-icon"
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onclick="copyToClipboard('fit_intercept',
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this.parentElement.nextElementSibling)"
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></i></td>
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<td class="param">
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|
<a class="param-doc-link"
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|
|
style="anchor-name: --doc-link-fit_intercept;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue">
|
|
|
fit_intercept
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-fit_intercept;">
|
|
|
fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>
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|
|
</a>
|
|
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</td>
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<td class="value">True</td>
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|
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</tr>
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<tr class="default">
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<td><i class="copy-paste-icon"
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onclick="copyToClipboard('intercept_scaling',
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this.parentElement.nextElementSibling)"
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></i></td>
|
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|
<td class="param">
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|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-intercept_scaling;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1">
|
|
|
intercept_scaling
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-intercept_scaling;">
|
|
|
intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a "synthetic" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br> The synthetic feature weight is subject to L1 or L2<br> regularization as all other features.<br> To lessen the effect of regularization on synthetic feature weight<br> (and therefore on the intercept) `intercept_scaling` has to be increased.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">1</td>
|
|
|
</tr>
|
|
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<tr class="default">
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|
|
<td><i class="copy-paste-icon"
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|
onclick="copyToClipboard('class_weight',
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|
|
this.parentElement.nextElementSibling)"
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|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-class_weight;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone">
|
|
|
class_weight
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-class_weight;">
|
|
|
class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The "balanced" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br> *class_weight='balanced'*</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">None</td>
|
|
|
</tr>
|
|
|
|
|
|
|
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|
<tr class="default">
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|
<td><i class="copy-paste-icon"
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onclick="copyToClipboard('solver',
|
|
|
this.parentElement.nextElementSibling)"
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|
|
></i></td>
|
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|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-solver;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27">
|
|
|
solver
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-solver;">
|
|
|
solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'}, default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br> class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br> 'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br> error.<br>- 'newton-cholesky' is a good choice for<br> `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br> categorical features with rare categories. Be aware that the memory usage<br> of this solver has a quadratic dependency on `n_features * n_classes`<br> because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br> and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br> one-versus-rest scheme for the multiclass setting one can wrap it with the<br> :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br> The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br> for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br> Elastic-Net) and on (multinomial) multiclass support:<br><br> ================= ======================== ======================<br> solver l1_ratio multinomial multiclass<br> ================= ======================== ======================<br> 'lbfgs' l1_ratio=0 yes<br> 'liblinear' l1_ratio=1 or l1_ratio=0 no<br> 'newton-cg' l1_ratio=0 yes<br> 'newton-cholesky' l1_ratio=0 yes<br> 'sag' l1_ratio=0 yes<br> 'saga' 0<=l1_ratio<=1 yes<br> ================= ======================== ======================<br><br>.. note::<br> 'sag' and 'saga' fast convergence is only guaranteed on features<br> with approximately the same scale. You can preprocess the data with<br> a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br> Refer to the :ref:`User Guide <Logistic_regression>` for more<br> information regarding :class:`LogisticRegression` and more specifically the<br> :ref:`Table <logistic_regression_solvers>`<br> summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br> Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br> version 0.18.<br>.. versionadded:: 0.19<br> SAGA solver.<br>.. versionchanged:: 0.22<br> The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br> newton-cholesky solver. Multinomial support in version 1.6.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">'lbfgs'</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
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|
|
onclick="copyToClipboard('max_iter',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-max_iter;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100">
|
|
|
max_iter
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-max_iter;">
|
|
|
max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">100</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
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|
|
onclick="copyToClipboard('verbose',
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|
|
this.parentElement.nextElementSibling)"
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|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-verbose;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0">
|
|
|
verbose
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-verbose;">
|
|
|
verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">0</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
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|
onclick="copyToClipboard('warm_start',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-warm_start;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse">
|
|
|
warm_start
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-warm_start;">
|
|
|
warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br> *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">False</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
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|
onclick="copyToClipboard('n_jobs',
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this.parentElement.nextElementSibling)"
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|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-n_jobs;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone">
|
|
|
n_jobs
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-n_jobs;">
|
|
|
n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br> `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">None</td>
|
|
|
</tr>
|
|
|
|
|
|
</tbody>
|
|
|
</table>
|
|
|
</details>
|
|
|
</div>
|
|
|
|
|
|
<div class="estimator-table">
|
|
|
<details>
|
|
|
<summary>Fitted attributes</summary>
|
|
|
<table class="parameters-table">
|
|
|
<tbody>
|
|
|
<tr>
|
|
|
<th>Name</th>
|
|
|
<th>Type</th>
|
|
|
<th>Value</th>
|
|
|
</tr>
|
|
|
|
|
|
<tr class="default">
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-classes_;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%20%29">
|
|
|
classes_
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-classes_;">
|
|
|
classes_: ndarray of shape (n_classes, )<br><br>A list of class labels known to the classifier.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="fitted-att-type">ndarray[int64](2,)</td>
|
|
|
<td>[0,1]</td>
|
|
|
|
|
|
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-coef_;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=coef_,-ndarray%20or%20CSR%20matrix%20of%20shape%20%281%2C%20n_features%29%20or%20%28n_classes%2C%20n_features%29">
|
|
|
coef_
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-coef_;">
|
|
|
coef_: ndarray or CSR matrix of shape (1, n_features) or (n_classes, n_features)<br><br>Coefficients of the features in the decision function.<br><br>`coef_` is of shape (1, n_features) when the given problem is binary.<br><br>By default, it will be created as a dense array, but can be turned to<br>sparse (CSR format) through :meth:`sparsify` (which can be beneficial<br>under L1 regularization when many coefficients are zero), and back to<br>dense through :meth:`densify`.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="fitted-att-type">ndarray[float64](1, 1)</td>
|
|
|
<td>[[-2.92]]</td>
|
|
|
|
|
|
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-intercept_;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20or%20%28n_classes%2C%29">
|
|
|
intercept_
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-intercept_;">
|
|
|
intercept_: ndarray of shape (1,) or (n_classes,)<br><br>Intercept (a.k.a. bias) added to the decision function.<br><br>If `fit_intercept` is set to False, the intercept is set to zero.<br>`intercept_` is of shape (1,) when the given problem is binary.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="fitted-att-type">ndarray[float64](1,)</td>
|
|
|
<td>[7.92]</td>
|
|
|
|
|
|
|
|
|
</tr>
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|
<tr class="default">
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<td class="param">
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|
<a class="param-doc-link"
|
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|
style="anchor-name: --doc-link-n_features_in_;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_features_in_,-int">
|
|
|
n_features_in_
|
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|
<span class="param-doc-description"
|
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|
style="position-anchor: --doc-link-n_features_in_;">
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|
|
n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="fitted-att-type">int</td>
|
|
|
<td>1</td>
|
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|
</tr>
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<tr class="default">
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<td class="param">
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|
<a class="param-doc-link"
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|
style="anchor-name: --doc-link-n_iter_;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_iter_,-ndarray%20of%20shape%20%281%2C%20%29">
|
|
|
n_iter_
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-n_iter_;">
|
|
|
n_iter_: ndarray of shape (1, )<br><br>Actual number of iterations for all classes.<br><br>.. versionchanged:: 0.20<br><br> In SciPy <= 1.0.0 the number of lbfgs iterations may exceed<br> ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.</span>
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|
|
</a>
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|
|
</td>
|
|
|
<td class="fitted-att-type">ndarray[int32](1,)</td>
|
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|
<td>[17]</td>
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</tr>
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|
</tbody>
|
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|
</table>
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|
</details>
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|
</div>
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</div></div></div></div></div><script>/* Authors: The scikit-learn developers
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SPDX-License-Identifier: BSD-3-Clause
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*/
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function copyToClipboard(text, element) {
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// Get the parameter prefix from the closest toggleable content
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const toggleableContent = element.closest('.sk-toggleable__content');
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const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';
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const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;
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const originalStyle = element.style;
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const computedStyle = window.getComputedStyle(element);
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const originalWidth = computedStyle.width;
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const originalHTML = element.innerHTML.replace('Copied!', '');
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navigator.clipboard.writeText(fullParamName)
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.then(() => {
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element.style.width = originalWidth;
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element.style.color = 'green';
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element.innerHTML = "Copied!";
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setTimeout(() => {
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element.innerHTML = originalHTML;
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element.style = originalStyle;
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}, 2000);
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})
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.catch(err => {
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console.error('Failed to copy:', err);
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element.style.color = 'red';
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element.innerHTML = "Failed!";
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setTimeout(() => {
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element.innerHTML = originalHTML;
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element.style = originalStyle;
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}, 2000);
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});
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return false;
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}
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document.querySelectorAll('.copy-paste-icon').forEach(function(element) {
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const toggleableContent = element.closest('.sk-toggleable__content');
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const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';
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const parent = element.parentElement;
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if (!parent || !parent.nextElementSibling) {
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console.warn('Expected copy-paste icon is missing from the DOM structure');
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return;
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}
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const paramName = element.parentElement.nextElementSibling
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.textContent.trim().split(' ')[0];
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const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;
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|
element.setAttribute('title', fullParamName);
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});
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|
/**
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|
* Copy the list of feature names formatted as a Python list.
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|
*
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|
* @param {HTMLElement} element - The copy button inside a `.features` block; its siblings
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|
* contain a `details` element and a table containing feature named.
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|
* @returns {boolean} Always returns `false` so callers can prevent the default click behavior.
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|
|
*/
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|
function copyFeatureNamesToClipboard(element) {
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|
var detailsElem = element.closest('.features').querySelector('details');
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|
var wasOpen = detailsElem.open;
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|
detailsElem.open = true;
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|
var content = element.closest('.features').querySelector('tbody')
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|
.innerText.trim();
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|
if (!wasOpen) detailsElem.open = false;
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|
|
const rows = content.split('\n').map(row => ` "${row}"`);
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|
const formattedText = `[\n${rows.join(',\n')},\n]`;
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|
const originalHTML = element.innerHTML.replace('✔', '');
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|
const originalStyle = element.style;
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const copyMark = document.createElement('span');
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|
copyMark.innerHTML = '✔';
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|
copyMark.style.color = 'blue';
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|
copyMark.style.fontSize = '1em';
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|
navigator.clipboard.writeText(formattedText)
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|
.then(() => {
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element.style.display = 'none';
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element.parentElement.appendChild(copyMark);
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setTimeout(() => {
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copyMark.remove();
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element.innerHTML = originalHTML;
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element.style = originalStyle;
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}, 1000);
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|
})
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|
.catch(err => {
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|
|
console.error('Failed to copy:', err);
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|
|
element.style.color = 'orange';
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|
|
element.innerHTML = "Failed!";
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|
setTimeout(() => {
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|
element.innerHTML = originalHTML;
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|
element.style = originalStyle;
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|
}, 1000);
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|
});
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return false;
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|
}
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|
/**
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|
|
* Adapted from Skrub
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|
|
* https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789
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|
* @returns "light" or "dark"
|
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|
*/
|
|
|
function detectTheme(element) {
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const body = document.querySelector('body');
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|
// Check VSCode theme
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const themeKindAttr = body.getAttribute('data-vscode-theme-kind');
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const themeNameAttr = body.getAttribute('data-vscode-theme-name');
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|
if (themeKindAttr && themeNameAttr) {
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const themeKind = themeKindAttr.toLowerCase();
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const themeName = themeNameAttr.toLowerCase();
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|
if (themeKind.includes("dark") || themeName.includes("dark")) {
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|
return "dark";
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|
}
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|
|
if (themeKind.includes("light") || themeName.includes("light")) {
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|
return "light";
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}
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}
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// Check Jupyter theme
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|
if (body.getAttribute('data-jp-theme-light') === 'false') {
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return 'dark';
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} else if (body.getAttribute('data-jp-theme-light') === 'true') {
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|
return 'light';
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|
|
}
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|
|
|
// Guess based on a parent element's color
|
|
|
const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');
|
|
|
const match = color.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)\s*$/i);
|
|
|
if (match) {
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|
|
const [r, g, b] = [
|
|
|
parseFloat(match[1]),
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|
parseFloat(match[2]),
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|
|
parseFloat(match[3])
|
|
|
];
|
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|
|
|
// https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness
|
|
|
const luma = 0.299 * r + 0.587 * g + 0.114 * b;
|
|
|
|
|
|
if (luma > 180) {
|
|
|
// If the text is very bright we have a dark theme
|
|
|
return 'dark';
|
|
|
}
|
|
|
if (luma < 75) {
|
|
|
// If the text is very dark we have a light theme
|
|
|
return 'light';
|
|
|
}
|
|
|
// Otherwise fall back to the next heuristic.
|
|
|
}
|
|
|
|
|
|
// Fallback to system preference
|
|
|
return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';
|
|
|
}
|
|
|
|
|
|
|
|
|
function forceTheme(elementId) {
|
|
|
const estimatorElement = document.querySelector(`#${elementId}`);
|
|
|
if (estimatorElement === null) {
|
|
|
console.error(`Element with id ${elementId} not found.`);
|
|
|
} else {
|
|
|
const theme = detectTheme(estimatorElement);
|
|
|
estimatorElement.classList.add(theme);
|
|
|
}
|
|
|
}
|
|
|
|
|
|
forceTheme('sk-container-id-2');</script></body>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
```python
|
|
|
Xnew = np.linspace(0,8,100).reshape(-1,1)
|
|
|
yPred = mylr.predict_proba(Xnew)
|
|
|
#plt.plot(Xnew, yPred[:,0], label= 'No Iris')
|
|
|
plt.plot(Xnew, yPred[:,1], label= 'Yes Iris')
|
|
|
plt.legend()
|
|
|
plt.plot(x,y,'*g')
|
|
|
plt.axis([1.5, 5, -0.5, 1.5])
|
|
|
plt.show()
|
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
This plot illustrates the performance of the second univariable model using **Petal Length**:
|
|
|
|
|
|
* **Sample Distribution:** Samples with short petal lengths (1.0 - 2.0 cm) are correctly clustered as *Iris setosa* ($y=1$), while samples with larger lengths ($>3.0$ cm) map to $y=0$.
|
|
|
* **Sigmoid Mapping:** The descending blue curve demonstrates that as petal length increases, the probability of the sample being *Iris setosa* drops sharply from 1.0 to 0.0.
|
|
|
* **Decision Boundary:** The curve crosses the 0.5 probability threshold at approximately 2.5 cm, marking the exact inflection point that separates the target class from the rest of the dataset.
|
|
|
|
|
|
### Model 3: Iris-Setosa Classifier based on Sepal length
|
|
|
|
|
|
#### Feature Shift – Sepal Length Isolation
|
|
|
|
|
|
The model evaluates a third morphological predictor independently:
|
|
|
* **Feature Vector ($X$):** Slicing index `[:, 0:1]` isolates **Sepal Length** as the continuous independent variable.
|
|
|
* **Target Continuity ($y$):** The objective remains focused on **Iris setosa** ($y=1$) to compare the separation power of sepal dimensions against the previous petal metrics.
|
|
|
|
|
|
|
|
|
```python
|
|
|
x = iris.data[:, 0:1]
|
|
|
y = (iris.target == 0).astype(int)
|
|
|
from sklearn.linear_model import LogisticRegression
|
|
|
mylr = LogisticRegression(solver='lbfgs', random_state=42)
|
|
|
mylr.fit(x,y)
|
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
<style>.sk-global {
|
|
|
/* Definition of color scheme common for light and dark mode */
|
|
|
--sklearn-color-text: #000;
|
|
|
--sklearn-color-text-muted: #666;
|
|
|
--sklearn-color-line: gray;
|
|
|
/* Definition of color scheme for unfitted estimators */
|
|
|
--sklearn-color-unfitted-level-0: #fff5e6;
|
|
|
--sklearn-color-unfitted-level-1: #f6e4d2;
|
|
|
--sklearn-color-unfitted-level-2: #ffe0b3;
|
|
|
--sklearn-color-unfitted-level-3: chocolate;
|
|
|
/* Definition of color scheme for fitted estimators */
|
|
|
--sklearn-color-fitted-level-0: #f0f8ff;
|
|
|
--sklearn-color-fitted-level-1: #d4ebff;
|
|
|
--sklearn-color-fitted-level-2: #b3dbfd;
|
|
|
--sklearn-color-fitted-level-3: cornflowerblue;
|
|
|
}
|
|
|
|
|
|
.sk-global.light {
|
|
|
/* Specific color for light theme */
|
|
|
--sklearn-color-text-on-default-background: black;
|
|
|
--sklearn-color-background: white;
|
|
|
--sklearn-color-border-box: black;
|
|
|
--sklearn-color-icon: #696969;
|
|
|
}
|
|
|
|
|
|
.sk-global.dark {
|
|
|
--sklearn-color-text-on-default-background: white;
|
|
|
--sklearn-color-background: #111;
|
|
|
--sklearn-color-border-box: white;
|
|
|
--sklearn-color-icon: #878787;
|
|
|
}
|
|
|
|
|
|
.sk-global {
|
|
|
color: var(--sklearn-color-text);
|
|
|
}
|
|
|
|
|
|
.sk-global pre {
|
|
|
padding: 0;
|
|
|
}
|
|
|
|
|
|
.sk-global input.sk-hidden--visually {
|
|
|
border: 0;
|
|
|
clip-path: inset(100%);
|
|
|
height: 1px;
|
|
|
margin: -1px;
|
|
|
overflow: hidden;
|
|
|
padding: 0;
|
|
|
position: absolute;
|
|
|
width: 1px;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-dashed-wrapped {
|
|
|
border: 1px dashed var(--sklearn-color-line);
|
|
|
margin: 0 0.4em 0.5em 0.4em;
|
|
|
box-sizing: border-box;
|
|
|
padding-bottom: 0.4em;
|
|
|
background-color: var(--sklearn-color-background);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-container {
|
|
|
/* jupyter's `normalize.less` sets `[hidden] { display: none; }`
|
|
|
but bootstrap.min.css set `[hidden] { display: none !important; }`
|
|
|
so we also need the `!important` here to be able to override the
|
|
|
default hidden behavior on the sphinx rendered scikit-learn.org.
|
|
|
See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
|
|
|
display: inline-block !important;
|
|
|
position: relative;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-text-repr-fallback {
|
|
|
display: none;
|
|
|
}
|
|
|
|
|
|
div.sk-parallel-item,
|
|
|
div.sk-serial,
|
|
|
div.sk-item {
|
|
|
/* draw centered vertical line to link estimators */
|
|
|
background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
|
|
|
background-size: 2px 100%;
|
|
|
background-repeat: no-repeat;
|
|
|
background-position: center center;
|
|
|
}
|
|
|
|
|
|
/* Parallel-specific style estimator block */
|
|
|
|
|
|
.sk-global div.sk-parallel-item::after {
|
|
|
content: "";
|
|
|
width: 100%;
|
|
|
border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
|
|
|
flex-grow: 1;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-parallel {
|
|
|
display: flex;
|
|
|
align-items: stretch;
|
|
|
justify-content: center;
|
|
|
background-color: var(--sklearn-color-background);
|
|
|
position: relative;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-parallel-item {
|
|
|
display: flex;
|
|
|
flex-direction: column;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-parallel-item:first-child::after {
|
|
|
align-self: flex-end;
|
|
|
width: 50%;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-parallel-item:last-child::after {
|
|
|
align-self: flex-start;
|
|
|
width: 50%;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-parallel-item:only-child::after {
|
|
|
width: 0;
|
|
|
}
|
|
|
|
|
|
/* Serial-specific style estimator block */
|
|
|
|
|
|
.sk-global div.sk-serial {
|
|
|
display: flex;
|
|
|
flex-direction: column;
|
|
|
align-items: center;
|
|
|
background-color: var(--sklearn-color-background);
|
|
|
padding-right: 1em;
|
|
|
padding-left: 1em;
|
|
|
}
|
|
|
|
|
|
|
|
|
/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
|
|
|
clickable and can be expanded/collapsed.
|
|
|
- Pipeline and ColumnTransformer use this feature and define the default style
|
|
|
- Estimators will overwrite some part of the style using the `sk-estimator` class
|
|
|
*/
|
|
|
|
|
|
/* Pipeline and ColumnTransformer style (default) */
|
|
|
|
|
|
.sk-global div.sk-toggleable {
|
|
|
/* Default theme specific background. It is overwritten whether we have a
|
|
|
specific estimator or a Pipeline/ColumnTransformer */
|
|
|
background-color: var(--sklearn-color-background);
|
|
|
}
|
|
|
|
|
|
/* Toggleable label */
|
|
|
.sk-global label.sk-toggleable__label {
|
|
|
cursor: pointer;
|
|
|
display: flex;
|
|
|
width: 100%;
|
|
|
margin-bottom: 0;
|
|
|
padding: 0.5em;
|
|
|
box-sizing: border-box;
|
|
|
text-align: center;
|
|
|
align-items: center;
|
|
|
justify-content: center;
|
|
|
gap: 0.5em;
|
|
|
}
|
|
|
|
|
|
.sk-global label.sk-toggleable__label .caption {
|
|
|
font-size: 0.6rem;
|
|
|
font-weight: lighter;
|
|
|
color: var(--sklearn-color-text-muted);
|
|
|
}
|
|
|
|
|
|
.sk-global label.sk-toggleable__label-arrow:before {
|
|
|
/* Arrow on the left of the label */
|
|
|
content: "▸";
|
|
|
float: left;
|
|
|
margin-right: 0.25em;
|
|
|
color: var(--sklearn-color-icon);
|
|
|
}
|
|
|
|
|
|
.sk-global label.sk-toggleable__label-arrow:hover:before {
|
|
|
color: var(--sklearn-color-text);
|
|
|
}
|
|
|
|
|
|
/* Toggleable content - dropdown */
|
|
|
|
|
|
.sk-global div.sk-toggleable__content {
|
|
|
display: none;
|
|
|
text-align: left;
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-toggleable__content.fitted {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-toggleable__content pre {
|
|
|
margin: 0.2em;
|
|
|
border-radius: 0.25em;
|
|
|
color: var(--sklearn-color-text);
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-toggleable__content.fitted pre {
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
}
|
|
|
|
|
|
.sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {
|
|
|
/* Expand drop-down */
|
|
|
display: block;
|
|
|
width: 100%;
|
|
|
overflow: visible;
|
|
|
}
|
|
|
|
|
|
.sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
|
|
|
content: "▾";
|
|
|
}
|
|
|
|
|
|
/* Pipeline/ColumnTransformer-specific style */
|
|
|
|
|
|
.sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
|
|
color: var(--sklearn-color-text);
|
|
|
background-color: var(--sklearn-color-unfitted-level-2);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
|
|
background-color: var(--sklearn-color-fitted-level-2);
|
|
|
}
|
|
|
|
|
|
/* Estimator-specific style */
|
|
|
|
|
|
/* Colorize estimator box */
|
|
|
.sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-2);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-2);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-label label.sk-toggleable__label,
|
|
|
.sk-global div.sk-label label {
|
|
|
/* The background is the default theme color */
|
|
|
color: var(--sklearn-color-text-on-default-background);
|
|
|
}
|
|
|
|
|
|
/* On hover, darken the color of the background */
|
|
|
.sk-global div.sk-label:hover label.sk-toggleable__label {
|
|
|
color: var(--sklearn-color-text);
|
|
|
background-color: var(--sklearn-color-unfitted-level-2);
|
|
|
}
|
|
|
|
|
|
/* Label box, darken color on hover, fitted */
|
|
|
.sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
|
|
|
color: var(--sklearn-color-text);
|
|
|
background-color: var(--sklearn-color-fitted-level-2);
|
|
|
}
|
|
|
|
|
|
/* Estimator label */
|
|
|
|
|
|
.sk-global div.sk-label label {
|
|
|
font-family: monospace;
|
|
|
font-weight: bold;
|
|
|
line-height: 1.2em;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-label-container {
|
|
|
text-align: center;
|
|
|
}
|
|
|
|
|
|
/* Estimator-specific */
|
|
|
.sk-global div.sk-estimator {
|
|
|
font-family: monospace;
|
|
|
border: 1px dotted var(--sklearn-color-border-box);
|
|
|
border-radius: 0.25em;
|
|
|
box-sizing: border-box;
|
|
|
margin-bottom: 0.5em;
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-estimator.fitted {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
}
|
|
|
|
|
|
/* on hover */
|
|
|
.sk-global div.sk-estimator:hover {
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-2);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-estimator.fitted:hover {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-2);
|
|
|
}
|
|
|
|
|
|
/* Specification for estimator info (e.g. "i" and "?") */
|
|
|
|
|
|
/* Common style for "i" and "?" */
|
|
|
|
|
|
.sk-estimator-doc-link,
|
|
|
a:link.sk-estimator-doc-link,
|
|
|
a:visited.sk-estimator-doc-link {
|
|
|
float: right;
|
|
|
font-size: smaller;
|
|
|
line-height: 1em;
|
|
|
font-family: monospace;
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
border-radius: 1em;
|
|
|
height: 1em;
|
|
|
width: 1em;
|
|
|
text-decoration: none !important;
|
|
|
margin-left: 0.5em;
|
|
|
text-align: center;
|
|
|
/* unfitted */
|
|
|
border: var(--sklearn-color-unfitted-level-3) 1pt solid;
|
|
|
color: var(--sklearn-color-unfitted-level-3);
|
|
|
}
|
|
|
|
|
|
.sk-estimator-doc-link.fitted,
|
|
|
a:link.sk-estimator-doc-link.fitted,
|
|
|
a:visited.sk-estimator-doc-link.fitted {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
border: var(--sklearn-color-fitted-level-3) 1pt solid;
|
|
|
color: var(--sklearn-color-fitted-level-3);
|
|
|
}
|
|
|
|
|
|
/* On hover */
|
|
|
div.sk-estimator:hover .sk-estimator-doc-link:hover,
|
|
|
.sk-estimator-doc-link:hover,
|
|
|
div.sk-label-container:hover .sk-estimator-doc-link:hover,
|
|
|
.sk-estimator-doc-link:hover {
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-3);
|
|
|
border: var(--sklearn-color-fitted-level-0) 1pt solid;
|
|
|
color: var(--sklearn-color-unfitted-level-0);
|
|
|
text-decoration: none;
|
|
|
}
|
|
|
|
|
|
div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
|
|
|
.sk-estimator-doc-link.fitted:hover,
|
|
|
div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
|
|
|
.sk-estimator-doc-link.fitted:hover {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-3);
|
|
|
border: var(--sklearn-color-fitted-level-0) 1pt solid;
|
|
|
color: var(--sklearn-color-fitted-level-0);
|
|
|
text-decoration: none;
|
|
|
}
|
|
|
|
|
|
/* Span, style for the box shown on hovering the info icon */
|
|
|
.sk-estimator-doc-link span {
|
|
|
display: none;
|
|
|
z-index: 9999;
|
|
|
position: relative;
|
|
|
font-weight: normal;
|
|
|
right: .2ex;
|
|
|
padding: .5ex;
|
|
|
margin: .5ex;
|
|
|
width: min-content;
|
|
|
min-width: 20ex;
|
|
|
max-width: 50ex;
|
|
|
color: var(--sklearn-color-text);
|
|
|
box-shadow: 2pt 2pt 4pt #999;
|
|
|
/* unfitted */
|
|
|
background: var(--sklearn-color-unfitted-level-0);
|
|
|
border: .5pt solid var(--sklearn-color-unfitted-level-3);
|
|
|
}
|
|
|
|
|
|
.sk-estimator-doc-link.fitted span {
|
|
|
/* fitted */
|
|
|
background: var(--sklearn-color-fitted-level-0);
|
|
|
border: var(--sklearn-color-fitted-level-3);
|
|
|
}
|
|
|
|
|
|
.sk-estimator-doc-link:hover span {
|
|
|
display: block;
|
|
|
}
|
|
|
|
|
|
/* "?"-specific style due to the `<a>` HTML tag */
|
|
|
|
|
|
.sk-global a.estimator_doc_link {
|
|
|
float: right;
|
|
|
font-size: 1rem;
|
|
|
line-height: 1em;
|
|
|
font-family: monospace;
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
border-radius: 1rem;
|
|
|
height: 1rem;
|
|
|
width: 1rem;
|
|
|
text-decoration: none;
|
|
|
/* unfitted */
|
|
|
color: var(--sklearn-color-unfitted-level-1);
|
|
|
border: var(--sklearn-color-unfitted-level-1) 1pt solid;
|
|
|
}
|
|
|
|
|
|
.sk-global a.estimator_doc_link.fitted {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
border: var(--sklearn-color-fitted-level-1) 1pt solid;
|
|
|
color: var(--sklearn-color-fitted-level-1);
|
|
|
}
|
|
|
|
|
|
/* On hover */
|
|
|
.sk-global a.estimator_doc_link:hover {
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-3);
|
|
|
color: var(--sklearn-color-background);
|
|
|
text-decoration: none;
|
|
|
}
|
|
|
|
|
|
.sk-global a.estimator_doc_link.fitted:hover {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-3);
|
|
|
}
|
|
|
|
|
|
.sk-top-container.sk-global {
|
|
|
/* pydata-sphinx-theme hides overflow, so scrolling is disabled.
|
|
|
We need to set it to !important and add tabindex="0" in the HTML
|
|
|
to allow keyboard-only users to navigate the display. */
|
|
|
overflow-x: scroll !important;
|
|
|
max-width: 100%;
|
|
|
}
|
|
|
|
|
|
.estimator-table {
|
|
|
font-family: monospace;
|
|
|
}
|
|
|
|
|
|
.estimator-table summary {
|
|
|
padding: .5rem;
|
|
|
cursor: pointer;
|
|
|
}
|
|
|
|
|
|
.estimator-table summary::marker {
|
|
|
font-size: 0.7rem;
|
|
|
}
|
|
|
|
|
|
.estimator-table details[open] {
|
|
|
padding-left: 0.1rem;
|
|
|
padding-right: 0.1rem;
|
|
|
padding-bottom: 0.3rem;
|
|
|
}
|
|
|
|
|
|
.estimator-table .parameters-table {
|
|
|
margin-left: auto !important;
|
|
|
margin-right: auto !important;
|
|
|
margin-top: 0;
|
|
|
}
|
|
|
|
|
|
.estimator-table .parameters-table tr:nth-child(odd) {
|
|
|
background-color: #fff;
|
|
|
}
|
|
|
|
|
|
.estimator-table .parameters-table tr:nth-child(even) {
|
|
|
background-color: #f6f6f6;
|
|
|
}
|
|
|
|
|
|
.estimator-table .parameters-table tr:hover td {
|
|
|
background-color: #e0e0e0;
|
|
|
}
|
|
|
|
|
|
.estimator-table table :is(td, th) {
|
|
|
border: 1px solid rgba(106, 105, 104, 0.232);
|
|
|
}
|
|
|
|
|
|
/*
|
|
|
`table td`is set in notebook with right text-align.
|
|
|
We need to overwrite it.
|
|
|
*/
|
|
|
.estimator-table table td.param {
|
|
|
text-align: left;
|
|
|
position: relative;
|
|
|
padding: 0;
|
|
|
}
|
|
|
|
|
|
.user-set td {
|
|
|
color:rgb(255, 94, 0);
|
|
|
text-align: left !important;
|
|
|
}
|
|
|
|
|
|
.user-set td.value {
|
|
|
color:rgb(255, 94, 0);
|
|
|
background-color: transparent;
|
|
|
}
|
|
|
|
|
|
.default td, .estimator-table th {
|
|
|
color: black;
|
|
|
text-align: left !important;
|
|
|
}
|
|
|
|
|
|
.user-set td i,
|
|
|
.default td i {
|
|
|
color: black;
|
|
|
}
|
|
|
|
|
|
td.fitted-att-type {
|
|
|
white-space: preserve nowrap;
|
|
|
}
|
|
|
|
|
|
/*
|
|
|
Styles for parameter documentation links
|
|
|
We need styling for visited so jupyter doesn't overwrite it
|
|
|
*/
|
|
|
a.param-doc-link,
|
|
|
a.param-doc-link:link,
|
|
|
a.param-doc-link:visited {
|
|
|
text-decoration: underline dashed;
|
|
|
text-underline-offset: .3em;
|
|
|
color: inherit;
|
|
|
display: block;
|
|
|
padding: .5em;
|
|
|
}
|
|
|
|
|
|
@supports(anchor-name: --doc-link) {
|
|
|
a.param-doc-link,
|
|
|
a.param-doc-link:link,
|
|
|
a.param-doc-link:visited {
|
|
|
anchor-name: --doc-link;
|
|
|
}
|
|
|
}
|
|
|
|
|
|
/* "hack" to make the entire area of the cell containing the link clickable */
|
|
|
a.param-doc-link::before {
|
|
|
position: absolute;
|
|
|
content: "";
|
|
|
inset: 0;
|
|
|
}
|
|
|
|
|
|
.param-doc-description {
|
|
|
display: none;
|
|
|
position: absolute;
|
|
|
z-index: 9999;
|
|
|
left: 0;
|
|
|
padding: .5ex;
|
|
|
margin-left: 1.5em;
|
|
|
color: var(--sklearn-color-text);
|
|
|
box-shadow: .3em .3em .4em #999;
|
|
|
width: max-content;
|
|
|
text-align: left;
|
|
|
max-height: 10em;
|
|
|
overflow-y: auto;
|
|
|
|
|
|
/* unfitted */
|
|
|
background: var(--sklearn-color-unfitted-level-0);
|
|
|
border: thin solid var(--sklearn-color-unfitted-level-3);
|
|
|
}
|
|
|
|
|
|
@supports(position-area: center right) {
|
|
|
.param-doc-description {
|
|
|
position-area: center right;
|
|
|
position: fixed;
|
|
|
margin-left: 0;
|
|
|
}
|
|
|
}
|
|
|
|
|
|
/* Fitted state for parameter tooltips */
|
|
|
.fitted .param-doc-description {
|
|
|
/* fitted */
|
|
|
background: var(--sklearn-color-fitted-level-0);
|
|
|
border: thin solid var(--sklearn-color-fitted-level-3);
|
|
|
}
|
|
|
|
|
|
.param-doc-link:hover .param-doc-description {
|
|
|
display: block;
|
|
|
}
|
|
|
|
|
|
.copy-paste-icon {
|
|
|
background-image: url(data:image/svg+xml;base64,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);
|
|
|
background-repeat: no-repeat;
|
|
|
background-size: 14px 14px;
|
|
|
background-position: 0;
|
|
|
display: inline-block;
|
|
|
width: 14px;
|
|
|
height: 14px;
|
|
|
cursor: pointer;
|
|
|
}
|
|
|
|
|
|
.features {
|
|
|
font-family: monospace;
|
|
|
cursor: pointer;
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background-color: var(--sklearn-color-unfitted-level-0);
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border: 1px dotted var(--sklearn-color-border-box);
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border-radius: .20em;
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margin-bottom: 0.5em;
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font-size: inherit; /* Needed for jupyter */
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}
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.features.fitted {
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background-color: var(--sklearn-color-fitted-level-0);
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}
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.features summary {
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cursor: pointer;
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display: flex;
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margin-bottom: 0;
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text-align: center;
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align-items: center;
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justify-content: center;
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gap: 0.5em;
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padding: .25em;
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}
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.features details[open] > summary {
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color: var(--sklearn-color-text);
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background-color: var(--sklearn-color-unfitted-level-2);
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border-radius: .20em 0 0 0;
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}
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.features.fitted details[open] > summary {
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background-color: var(--sklearn-color-fitted-level-2);
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.features details > summary .arrow::before {
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color: grey;
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.features details[open] > summary .arrow::before {
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margin: 0;
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overflow: auto;
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background-color: var(--sklearn-color-fitted-level-0);
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}
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background-size: 1em 1em;
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width: 1em;
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height: 1em;
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filter: grayscale(100%) opacity(60%);
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}
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width: 100%;
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.features .features-container table tr:nth-child(odd) {
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background-color: #fff;
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.features .features-container table tr:nth-child(even) {
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background-color: #f6f6f6;
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}
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.features .features-container table tr:hover {
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background-color: #e0e0e0;
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}
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.features .features-container table {
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table-layout: inherit;
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text-align: left;
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padding: 0 0.5em;
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border: 1px solid rgba(106, 105, 104, 0.232);
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white-space: nowrap;
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color: var(--sklearn-color-text);
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}
|
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.total_features {
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display: flex;
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justify-content: center;
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margin-top: 0.5em;
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}
|
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</style><body><div id="sk-container-id-3" tabindex="0" class="sk-top-container sk-global"><div class="sk-text-repr-fallback"><pre>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually sk-global" id="sk-estimator-id-3" type="checkbox" checked><label for="sk-estimator-id-3" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted" data-param-prefix="">
|
|
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<div class="estimator-table">
|
|
|
<details>
|
|
|
<summary>Parameters</summary>
|
|
|
<table class="parameters-table">
|
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|
<tbody>
|
|
|
|
|
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<tr class="user-set">
|
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<td><i class="copy-paste-icon"
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|
|
onclick="copyToClipboard('random_state',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-random_state;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone">
|
|
|
random_state
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-random_state;">
|
|
|
random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">42</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('penalty',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-penalty;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27">
|
|
|
penalty
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-penalty;">
|
|
|
penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add an L2 penalty term and it is the default choice;<br>- `'l1'`: add an L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br> Some penalties may not work with some solvers. See the parameter<br> `solver` below, to know the compatibility between the penalty and<br> solver.<br><br>.. versionadded:: 0.19<br> l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br> `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br> Use `l1_ratio` and `C` instead. `l1_ratio=0` for `penalty='l2'`,<br> `l1_ratio=1` for `penalty='l1'`, `l1_ratio` set to any float between 0 and 1<br> for `penalty='elasticnet'`, and `C=np.inf` for `penalty=None`.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">'deprecated'</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('C',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-C;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0">
|
|
|
C
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-C;">
|
|
|
C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">1.0</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('l1_ratio',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-l1_ratio;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0">
|
|
|
l1_ratio
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-l1_ratio;">
|
|
|
l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br> Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br> solvers. See the parameter `solver` below, to know the compatibility between<br> the penalty and solver.<br><br>.. versionchanged:: 1.8<br> Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br> `None` is deprecated and will be removed in version 1.10. Always use<br> `l1_ratio` to specify the penalty type.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">0.0</td>
|
|
|
</tr>
|
|
|
|
|
|
|
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|
<tr class="default">
|
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|
<td><i class="copy-paste-icon"
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|
onclick="copyToClipboard('dual',
|
|
|
this.parentElement.nextElementSibling)"
|
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|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-dual;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse">
|
|
|
dual
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-dual;">
|
|
|
dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">False</td>
|
|
|
</tr>
|
|
|
|
|
|
|
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|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
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|
onclick="copyToClipboard('tol',
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this.parentElement.nextElementSibling)"
|
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></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-tol;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4">
|
|
|
tol
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-tol;">
|
|
|
tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">0.0001</td>
|
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|
</tr>
|
|
|
|
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|
<tr class="default">
|
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|
<td><i class="copy-paste-icon"
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|
onclick="copyToClipboard('fit_intercept',
|
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|
this.parentElement.nextElementSibling)"
|
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></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-fit_intercept;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue">
|
|
|
fit_intercept
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-fit_intercept;">
|
|
|
fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">True</td>
|
|
|
</tr>
|
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|
|
|
|
|
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|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
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|
onclick="copyToClipboard('intercept_scaling',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-intercept_scaling;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1">
|
|
|
intercept_scaling
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-intercept_scaling;">
|
|
|
intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a "synthetic" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br> The synthetic feature weight is subject to L1 or L2<br> regularization as all other features.<br> To lessen the effect of regularization on synthetic feature weight<br> (and therefore on the intercept) `intercept_scaling` has to be increased.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">1</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('class_weight',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-class_weight;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone">
|
|
|
class_weight
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-class_weight;">
|
|
|
class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The "balanced" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br> *class_weight='balanced'*</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">None</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('solver',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-solver;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27">
|
|
|
solver
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-solver;">
|
|
|
solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'}, default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br> class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br> 'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br> error.<br>- 'newton-cholesky' is a good choice for<br> `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br> categorical features with rare categories. Be aware that the memory usage<br> of this solver has a quadratic dependency on `n_features * n_classes`<br> because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br> and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br> one-versus-rest scheme for the multiclass setting one can wrap it with the<br> :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br> The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br> for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br> Elastic-Net) and on (multinomial) multiclass support:<br><br> ================= ======================== ======================<br> solver l1_ratio multinomial multiclass<br> ================= ======================== ======================<br> 'lbfgs' l1_ratio=0 yes<br> 'liblinear' l1_ratio=1 or l1_ratio=0 no<br> 'newton-cg' l1_ratio=0 yes<br> 'newton-cholesky' l1_ratio=0 yes<br> 'sag' l1_ratio=0 yes<br> 'saga' 0<=l1_ratio<=1 yes<br> ================= ======================== ======================<br><br>.. note::<br> 'sag' and 'saga' fast convergence is only guaranteed on features<br> with approximately the same scale. You can preprocess the data with<br> a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br> Refer to the :ref:`User Guide <Logistic_regression>` for more<br> information regarding :class:`LogisticRegression` and more specifically the<br> :ref:`Table <logistic_regression_solvers>`<br> summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br> Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br> version 0.18.<br>.. versionadded:: 0.19<br> SAGA solver.<br>.. versionchanged:: 0.22<br> The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br> newton-cholesky solver. Multinomial support in version 1.6.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">'lbfgs'</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
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|
|
onclick="copyToClipboard('max_iter',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-max_iter;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100">
|
|
|
max_iter
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-max_iter;">
|
|
|
max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">100</td>
|
|
|
</tr>
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|
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|
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|
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|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
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|
|
onclick="copyToClipboard('verbose',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-verbose;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0">
|
|
|
verbose
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-verbose;">
|
|
|
verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">0</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
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|
onclick="copyToClipboard('warm_start',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-warm_start;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse">
|
|
|
warm_start
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-warm_start;">
|
|
|
warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br> *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">False</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('n_jobs',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-n_jobs;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone">
|
|
|
n_jobs
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-n_jobs;">
|
|
|
n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br> `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">None</td>
|
|
|
</tr>
|
|
|
|
|
|
</tbody>
|
|
|
</table>
|
|
|
</details>
|
|
|
</div>
|
|
|
|
|
|
<div class="estimator-table">
|
|
|
<details>
|
|
|
<summary>Fitted attributes</summary>
|
|
|
<table class="parameters-table">
|
|
|
<tbody>
|
|
|
<tr>
|
|
|
<th>Name</th>
|
|
|
<th>Type</th>
|
|
|
<th>Value</th>
|
|
|
</tr>
|
|
|
|
|
|
<tr class="default">
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-classes_;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%20%29">
|
|
|
classes_
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-classes_;">
|
|
|
classes_: ndarray of shape (n_classes, )<br><br>A list of class labels known to the classifier.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="fitted-att-type">ndarray[int64](2,)</td>
|
|
|
<td>[0,1]</td>
|
|
|
|
|
|
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-coef_;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=coef_,-ndarray%20or%20CSR%20matrix%20of%20shape%20%281%2C%20n_features%29%20or%20%28n_classes%2C%20n_features%29">
|
|
|
coef_
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-coef_;">
|
|
|
coef_: ndarray or CSR matrix of shape (1, n_features) or (n_classes, n_features)<br><br>Coefficients of the features in the decision function.<br><br>`coef_` is of shape (1, n_features) when the given problem is binary.<br><br>By default, it will be created as a dense array, but can be turned to<br>sparse (CSR format) through :meth:`sparsify` (which can be beneficial<br>under L1 regularization when many coefficients are zero), and back to<br>dense through :meth:`densify`.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="fitted-att-type">ndarray[float64](1, 1)</td>
|
|
|
<td>[[-3.46]]</td>
|
|
|
|
|
|
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-intercept_;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20or%20%28n_classes%2C%29">
|
|
|
intercept_
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-intercept_;">
|
|
|
intercept_: ndarray of shape (1,) or (n_classes,)<br><br>Intercept (a.k.a. bias) added to the decision function.<br><br>If `fit_intercept` is set to False, the intercept is set to zero.<br>`intercept_` is of shape (1,) when the given problem is binary.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="fitted-att-type">ndarray[float64](1,)</td>
|
|
|
<td>[18.55]</td>
|
|
|
|
|
|
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-n_features_in_;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_features_in_,-int">
|
|
|
n_features_in_
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-n_features_in_;">
|
|
|
n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="fitted-att-type">int</td>
|
|
|
<td>1</td>
|
|
|
|
|
|
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-n_iter_;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_iter_,-ndarray%20of%20shape%20%281%2C%20%29">
|
|
|
n_iter_
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-n_iter_;">
|
|
|
n_iter_: ndarray of shape (1, )<br><br>Actual number of iterations for all classes.<br><br>.. versionchanged:: 0.20<br><br> In SciPy <= 1.0.0 the number of lbfgs iterations may exceed<br> ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="fitted-att-type">ndarray[int32](1,)</td>
|
|
|
<td>[17]</td>
|
|
|
|
|
|
|
|
|
</tr>
|
|
|
|
|
|
</tbody>
|
|
|
</table>
|
|
|
</details>
|
|
|
</div>
|
|
|
</div></div></div></div></div><script>/* Authors: The scikit-learn developers
|
|
|
SPDX-License-Identifier: BSD-3-Clause
|
|
|
*/
|
|
|
|
|
|
function copyToClipboard(text, element) {
|
|
|
// Get the parameter prefix from the closest toggleable content
|
|
|
const toggleableContent = element.closest('.sk-toggleable__content');
|
|
|
const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';
|
|
|
const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;
|
|
|
|
|
|
const originalStyle = element.style;
|
|
|
const computedStyle = window.getComputedStyle(element);
|
|
|
const originalWidth = computedStyle.width;
|
|
|
const originalHTML = element.innerHTML.replace('Copied!', '');
|
|
|
|
|
|
navigator.clipboard.writeText(fullParamName)
|
|
|
.then(() => {
|
|
|
element.style.width = originalWidth;
|
|
|
element.style.color = 'green';
|
|
|
element.innerHTML = "Copied!";
|
|
|
|
|
|
setTimeout(() => {
|
|
|
element.innerHTML = originalHTML;
|
|
|
element.style = originalStyle;
|
|
|
}, 2000);
|
|
|
})
|
|
|
.catch(err => {
|
|
|
console.error('Failed to copy:', err);
|
|
|
element.style.color = 'red';
|
|
|
element.innerHTML = "Failed!";
|
|
|
setTimeout(() => {
|
|
|
element.innerHTML = originalHTML;
|
|
|
element.style = originalStyle;
|
|
|
}, 2000);
|
|
|
});
|
|
|
return false;
|
|
|
}
|
|
|
|
|
|
document.querySelectorAll('.copy-paste-icon').forEach(function(element) {
|
|
|
const toggleableContent = element.closest('.sk-toggleable__content');
|
|
|
const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';
|
|
|
|
|
|
const parent = element.parentElement;
|
|
|
if (!parent || !parent.nextElementSibling) {
|
|
|
console.warn('Expected copy-paste icon is missing from the DOM structure');
|
|
|
return;
|
|
|
}
|
|
|
|
|
|
const paramName = element.parentElement.nextElementSibling
|
|
|
.textContent.trim().split(' ')[0];
|
|
|
const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;
|
|
|
|
|
|
element.setAttribute('title', fullParamName);
|
|
|
});
|
|
|
|
|
|
/**
|
|
|
* Copy the list of feature names formatted as a Python list.
|
|
|
*
|
|
|
* @param {HTMLElement} element - The copy button inside a `.features` block; its siblings
|
|
|
* contain a `details` element and a table containing feature named.
|
|
|
* @returns {boolean} Always returns `false` so callers can prevent the default click behavior.
|
|
|
*/
|
|
|
function copyFeatureNamesToClipboard(element) {
|
|
|
var detailsElem = element.closest('.features').querySelector('details');
|
|
|
var wasOpen = detailsElem.open;
|
|
|
detailsElem.open = true;
|
|
|
var content = element.closest('.features').querySelector('tbody')
|
|
|
.innerText.trim();
|
|
|
if (!wasOpen) detailsElem.open = false;
|
|
|
const rows = content.split('\n').map(row => ` "${row}"`);
|
|
|
const formattedText = `[\n${rows.join(',\n')},\n]`;
|
|
|
const originalHTML = element.innerHTML.replace('✔', '');
|
|
|
const originalStyle = element.style;
|
|
|
const copyMark = document.createElement('span');
|
|
|
copyMark.innerHTML = '✔';
|
|
|
copyMark.style.color = 'blue';
|
|
|
copyMark.style.fontSize = '1em';
|
|
|
|
|
|
navigator.clipboard.writeText(formattedText)
|
|
|
.then(() => {
|
|
|
element.style.display = 'none';
|
|
|
element.parentElement.appendChild(copyMark);
|
|
|
|
|
|
setTimeout(() => {
|
|
|
copyMark.remove();
|
|
|
element.innerHTML = originalHTML;
|
|
|
element.style = originalStyle;
|
|
|
}, 1000);
|
|
|
})
|
|
|
.catch(err => {
|
|
|
console.error('Failed to copy:', err);
|
|
|
element.style.color = 'orange';
|
|
|
element.innerHTML = "Failed!";
|
|
|
setTimeout(() => {
|
|
|
element.innerHTML = originalHTML;
|
|
|
element.style = originalStyle;
|
|
|
}, 1000);
|
|
|
});
|
|
|
return false;
|
|
|
}
|
|
|
/**
|
|
|
* Adapted from Skrub
|
|
|
* https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789
|
|
|
* @returns "light" or "dark"
|
|
|
*/
|
|
|
function detectTheme(element) {
|
|
|
const body = document.querySelector('body');
|
|
|
|
|
|
// Check VSCode theme
|
|
|
const themeKindAttr = body.getAttribute('data-vscode-theme-kind');
|
|
|
const themeNameAttr = body.getAttribute('data-vscode-theme-name');
|
|
|
|
|
|
if (themeKindAttr && themeNameAttr) {
|
|
|
const themeKind = themeKindAttr.toLowerCase();
|
|
|
const themeName = themeNameAttr.toLowerCase();
|
|
|
|
|
|
if (themeKind.includes("dark") || themeName.includes("dark")) {
|
|
|
return "dark";
|
|
|
}
|
|
|
if (themeKind.includes("light") || themeName.includes("light")) {
|
|
|
return "light";
|
|
|
}
|
|
|
}
|
|
|
|
|
|
// Check Jupyter theme
|
|
|
if (body.getAttribute('data-jp-theme-light') === 'false') {
|
|
|
return 'dark';
|
|
|
} else if (body.getAttribute('data-jp-theme-light') === 'true') {
|
|
|
return 'light';
|
|
|
}
|
|
|
|
|
|
// Guess based on a parent element's color
|
|
|
const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');
|
|
|
const match = color.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)\s*$/i);
|
|
|
if (match) {
|
|
|
const [r, g, b] = [
|
|
|
parseFloat(match[1]),
|
|
|
parseFloat(match[2]),
|
|
|
parseFloat(match[3])
|
|
|
];
|
|
|
|
|
|
// https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness
|
|
|
const luma = 0.299 * r + 0.587 * g + 0.114 * b;
|
|
|
|
|
|
if (luma > 180) {
|
|
|
// If the text is very bright we have a dark theme
|
|
|
return 'dark';
|
|
|
}
|
|
|
if (luma < 75) {
|
|
|
// If the text is very dark we have a light theme
|
|
|
return 'light';
|
|
|
}
|
|
|
// Otherwise fall back to the next heuristic.
|
|
|
}
|
|
|
|
|
|
// Fallback to system preference
|
|
|
return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';
|
|
|
}
|
|
|
|
|
|
|
|
|
function forceTheme(elementId) {
|
|
|
const estimatorElement = document.querySelector(`#${elementId}`);
|
|
|
if (estimatorElement === null) {
|
|
|
console.error(`Element with id ${elementId} not found.`);
|
|
|
} else {
|
|
|
const theme = detectTheme(estimatorElement);
|
|
|
estimatorElement.classList.add(theme);
|
|
|
}
|
|
|
}
|
|
|
|
|
|
forceTheme('sk-container-id-3');</script></body>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
```python
|
|
|
Xnew = np.linspace(0,8,100).reshape(-1,1)
|
|
|
yPred = mylr.predict_proba(Xnew)
|
|
|
#plt.plot(Xnew, yPred[:,0], label= 'No Iris')
|
|
|
plt.plot(Xnew, yPred[:,1], label= 'Yes Iris')
|
|
|
plt.legend()
|
|
|
plt.plot(x,y,'*g')
|
|
|
plt.axis([3.5, 7, -0.1, 1.1])
|
|
|
plt.show()
|
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
This plot displays the performance of the third univariable model using **Sepal Length**:
|
|
|
|
|
|
* **Sample Distribution:** Samples representing *Iris setosa* ($y=1$) are concentrated at shorter lengths, but show a much higher spatial overlap with non-setosa samples ($y=0$) compared to the previous petal features.
|
|
|
* **Sigmoid Mapping:** The descending curve shows the probability dropping as sepal length increases. Due to this significant data overlap, the slope is less steep, indicating a more gradual and less aggressive probabilistic transition.
|
|
|
* **Decision Boundary:** The inflection point at $\sigma = 0.5$ establishes the final threshold. This boundary carries more classification uncertainty because sepal dimensions are naturally less distinct between these species.
|
|
|
|
|
|
### Model 4: Multiple features classifier
|
|
|
|
|
|
#### Multi-Class Spatial Mapping (Sepal Features)
|
|
|
|
|
|
This cell upgrades the initial exploratory plot by adding the ground-truth class labels to the 2D sepal feature space:
|
|
|
|
|
|
* **Feature Interaction:** Maps Sepal Length (`sl`) against Sepal Width (`sw`) simultaneously to analyze their combined distribution.
|
|
|
* **Class Color-Coding:** Differentiates the three original species using distinct markers: Green for *Setosa*, Red for *Versicolor*, and Blue for *Virginica*.
|
|
|
* **Visual Separability Analysis:** Allows immediate observation of the data structure, showing that while *Setosa* forms a perfectly isolated cluster, *Versicolor* and *Virginica* exhibit significant spatial overlap, justifying the need for optimization models.
|
|
|
|
|
|
|
|
|
```python
|
|
|
import matplotlib.pyplot as plt
|
|
|
sl = iris.data[:,0:1]
|
|
|
sw = iris.data[:,1:2]
|
|
|
tg = iris.target
|
|
|
plt.plot(sl[tg==0,0], sw[tg==0,0],'.g' ,label='Set')
|
|
|
plt.plot(sl[tg==1,0], sw[tg==1,0],'.r', label='Ver')
|
|
|
plt.plot(sl[tg==2,0], sw[tg==2,0],'.b', label='Vir')
|
|
|
plt.legend()
|
|
|
plt.show()
|
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
#### Bivariate Model Training for Iris Virginica
|
|
|
|
|
|
This cell configures and trains a multi-feature logistic regression model utilizing tuned optimization parameters:
|
|
|
|
|
|
* **Bivariate Data Selection:** * **Features (`X`):** Slices index `[:, 0:2]` to combine **Sepal Length** and **Sepal Width** into a two-dimensional feature space.
|
|
|
* **Target (`y`):** Shifts the positive class focus exclusively to **Iris virginica** (`iris.target == 2`).
|
|
|
* **Hyperparameter Tuning (`mylrvir`):**
|
|
|
* **`solver='newton-cg'`**: Uses the Newton-Conjugate Gradient method to compute accurate optimization paths.
|
|
|
* **`C=100` & `tol=1e-5`**: Applies high cost (low regularization) to allow a tighter fit to the data, paired with a strict tolerance for precise convergence.
|
|
|
* **`mylrvir.fit(X, y)`**: Trains the system to find the optimal weight vector $w = [w_1, w_2]$ and bias ($b$), establishing the multi-variable benchmark line.
|
|
|
|
|
|
|
|
|
```python
|
|
|
X = iris.data[:,0:2]
|
|
|
y = (iris.target==2).astype(int)
|
|
|
mylrvir = LogisticRegression(
|
|
|
random_state=22,
|
|
|
tol=1e-5,
|
|
|
C=100,
|
|
|
max_iter=100,
|
|
|
solver='newton-cg'
|
|
|
)
|
|
|
mylrvir.fit(X,y)
|
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
<style>.sk-global {
|
|
|
/* Definition of color scheme common for light and dark mode */
|
|
|
--sklearn-color-text: #000;
|
|
|
--sklearn-color-text-muted: #666;
|
|
|
--sklearn-color-line: gray;
|
|
|
/* Definition of color scheme for unfitted estimators */
|
|
|
--sklearn-color-unfitted-level-0: #fff5e6;
|
|
|
--sklearn-color-unfitted-level-1: #f6e4d2;
|
|
|
--sklearn-color-unfitted-level-2: #ffe0b3;
|
|
|
--sklearn-color-unfitted-level-3: chocolate;
|
|
|
/* Definition of color scheme for fitted estimators */
|
|
|
--sklearn-color-fitted-level-0: #f0f8ff;
|
|
|
--sklearn-color-fitted-level-1: #d4ebff;
|
|
|
--sklearn-color-fitted-level-2: #b3dbfd;
|
|
|
--sklearn-color-fitted-level-3: cornflowerblue;
|
|
|
}
|
|
|
|
|
|
.sk-global.light {
|
|
|
/* Specific color for light theme */
|
|
|
--sklearn-color-text-on-default-background: black;
|
|
|
--sklearn-color-background: white;
|
|
|
--sklearn-color-border-box: black;
|
|
|
--sklearn-color-icon: #696969;
|
|
|
}
|
|
|
|
|
|
.sk-global.dark {
|
|
|
--sklearn-color-text-on-default-background: white;
|
|
|
--sklearn-color-background: #111;
|
|
|
--sklearn-color-border-box: white;
|
|
|
--sklearn-color-icon: #878787;
|
|
|
}
|
|
|
|
|
|
.sk-global {
|
|
|
color: var(--sklearn-color-text);
|
|
|
}
|
|
|
|
|
|
.sk-global pre {
|
|
|
padding: 0;
|
|
|
}
|
|
|
|
|
|
.sk-global input.sk-hidden--visually {
|
|
|
border: 0;
|
|
|
clip-path: inset(100%);
|
|
|
height: 1px;
|
|
|
margin: -1px;
|
|
|
overflow: hidden;
|
|
|
padding: 0;
|
|
|
position: absolute;
|
|
|
width: 1px;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-dashed-wrapped {
|
|
|
border: 1px dashed var(--sklearn-color-line);
|
|
|
margin: 0 0.4em 0.5em 0.4em;
|
|
|
box-sizing: border-box;
|
|
|
padding-bottom: 0.4em;
|
|
|
background-color: var(--sklearn-color-background);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-container {
|
|
|
/* jupyter's `normalize.less` sets `[hidden] { display: none; }`
|
|
|
but bootstrap.min.css set `[hidden] { display: none !important; }`
|
|
|
so we also need the `!important` here to be able to override the
|
|
|
default hidden behavior on the sphinx rendered scikit-learn.org.
|
|
|
See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
|
|
|
display: inline-block !important;
|
|
|
position: relative;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-text-repr-fallback {
|
|
|
display: none;
|
|
|
}
|
|
|
|
|
|
div.sk-parallel-item,
|
|
|
div.sk-serial,
|
|
|
div.sk-item {
|
|
|
/* draw centered vertical line to link estimators */
|
|
|
background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
|
|
|
background-size: 2px 100%;
|
|
|
background-repeat: no-repeat;
|
|
|
background-position: center center;
|
|
|
}
|
|
|
|
|
|
/* Parallel-specific style estimator block */
|
|
|
|
|
|
.sk-global div.sk-parallel-item::after {
|
|
|
content: "";
|
|
|
width: 100%;
|
|
|
border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
|
|
|
flex-grow: 1;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-parallel {
|
|
|
display: flex;
|
|
|
align-items: stretch;
|
|
|
justify-content: center;
|
|
|
background-color: var(--sklearn-color-background);
|
|
|
position: relative;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-parallel-item {
|
|
|
display: flex;
|
|
|
flex-direction: column;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-parallel-item:first-child::after {
|
|
|
align-self: flex-end;
|
|
|
width: 50%;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-parallel-item:last-child::after {
|
|
|
align-self: flex-start;
|
|
|
width: 50%;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-parallel-item:only-child::after {
|
|
|
width: 0;
|
|
|
}
|
|
|
|
|
|
/* Serial-specific style estimator block */
|
|
|
|
|
|
.sk-global div.sk-serial {
|
|
|
display: flex;
|
|
|
flex-direction: column;
|
|
|
align-items: center;
|
|
|
background-color: var(--sklearn-color-background);
|
|
|
padding-right: 1em;
|
|
|
padding-left: 1em;
|
|
|
}
|
|
|
|
|
|
|
|
|
/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
|
|
|
clickable and can be expanded/collapsed.
|
|
|
- Pipeline and ColumnTransformer use this feature and define the default style
|
|
|
- Estimators will overwrite some part of the style using the `sk-estimator` class
|
|
|
*/
|
|
|
|
|
|
/* Pipeline and ColumnTransformer style (default) */
|
|
|
|
|
|
.sk-global div.sk-toggleable {
|
|
|
/* Default theme specific background. It is overwritten whether we have a
|
|
|
specific estimator or a Pipeline/ColumnTransformer */
|
|
|
background-color: var(--sklearn-color-background);
|
|
|
}
|
|
|
|
|
|
/* Toggleable label */
|
|
|
.sk-global label.sk-toggleable__label {
|
|
|
cursor: pointer;
|
|
|
display: flex;
|
|
|
width: 100%;
|
|
|
margin-bottom: 0;
|
|
|
padding: 0.5em;
|
|
|
box-sizing: border-box;
|
|
|
text-align: center;
|
|
|
align-items: center;
|
|
|
justify-content: center;
|
|
|
gap: 0.5em;
|
|
|
}
|
|
|
|
|
|
.sk-global label.sk-toggleable__label .caption {
|
|
|
font-size: 0.6rem;
|
|
|
font-weight: lighter;
|
|
|
color: var(--sklearn-color-text-muted);
|
|
|
}
|
|
|
|
|
|
.sk-global label.sk-toggleable__label-arrow:before {
|
|
|
/* Arrow on the left of the label */
|
|
|
content: "▸";
|
|
|
float: left;
|
|
|
margin-right: 0.25em;
|
|
|
color: var(--sklearn-color-icon);
|
|
|
}
|
|
|
|
|
|
.sk-global label.sk-toggleable__label-arrow:hover:before {
|
|
|
color: var(--sklearn-color-text);
|
|
|
}
|
|
|
|
|
|
/* Toggleable content - dropdown */
|
|
|
|
|
|
.sk-global div.sk-toggleable__content {
|
|
|
display: none;
|
|
|
text-align: left;
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-toggleable__content.fitted {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-toggleable__content pre {
|
|
|
margin: 0.2em;
|
|
|
border-radius: 0.25em;
|
|
|
color: var(--sklearn-color-text);
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-toggleable__content.fitted pre {
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
}
|
|
|
|
|
|
.sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {
|
|
|
/* Expand drop-down */
|
|
|
display: block;
|
|
|
width: 100%;
|
|
|
overflow: visible;
|
|
|
}
|
|
|
|
|
|
.sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
|
|
|
content: "▾";
|
|
|
}
|
|
|
|
|
|
/* Pipeline/ColumnTransformer-specific style */
|
|
|
|
|
|
.sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
|
|
color: var(--sklearn-color-text);
|
|
|
background-color: var(--sklearn-color-unfitted-level-2);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
|
|
background-color: var(--sklearn-color-fitted-level-2);
|
|
|
}
|
|
|
|
|
|
/* Estimator-specific style */
|
|
|
|
|
|
/* Colorize estimator box */
|
|
|
.sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-2);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-2);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-label label.sk-toggleable__label,
|
|
|
.sk-global div.sk-label label {
|
|
|
/* The background is the default theme color */
|
|
|
color: var(--sklearn-color-text-on-default-background);
|
|
|
}
|
|
|
|
|
|
/* On hover, darken the color of the background */
|
|
|
.sk-global div.sk-label:hover label.sk-toggleable__label {
|
|
|
color: var(--sklearn-color-text);
|
|
|
background-color: var(--sklearn-color-unfitted-level-2);
|
|
|
}
|
|
|
|
|
|
/* Label box, darken color on hover, fitted */
|
|
|
.sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
|
|
|
color: var(--sklearn-color-text);
|
|
|
background-color: var(--sklearn-color-fitted-level-2);
|
|
|
}
|
|
|
|
|
|
/* Estimator label */
|
|
|
|
|
|
.sk-global div.sk-label label {
|
|
|
font-family: monospace;
|
|
|
font-weight: bold;
|
|
|
line-height: 1.2em;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-label-container {
|
|
|
text-align: center;
|
|
|
}
|
|
|
|
|
|
/* Estimator-specific */
|
|
|
.sk-global div.sk-estimator {
|
|
|
font-family: monospace;
|
|
|
border: 1px dotted var(--sklearn-color-border-box);
|
|
|
border-radius: 0.25em;
|
|
|
box-sizing: border-box;
|
|
|
margin-bottom: 0.5em;
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-estimator.fitted {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
}
|
|
|
|
|
|
/* on hover */
|
|
|
.sk-global div.sk-estimator:hover {
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-2);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-estimator.fitted:hover {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-2);
|
|
|
}
|
|
|
|
|
|
/* Specification for estimator info (e.g. "i" and "?") */
|
|
|
|
|
|
/* Common style for "i" and "?" */
|
|
|
|
|
|
.sk-estimator-doc-link,
|
|
|
a:link.sk-estimator-doc-link,
|
|
|
a:visited.sk-estimator-doc-link {
|
|
|
float: right;
|
|
|
font-size: smaller;
|
|
|
line-height: 1em;
|
|
|
font-family: monospace;
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
border-radius: 1em;
|
|
|
height: 1em;
|
|
|
width: 1em;
|
|
|
text-decoration: none !important;
|
|
|
margin-left: 0.5em;
|
|
|
text-align: center;
|
|
|
/* unfitted */
|
|
|
border: var(--sklearn-color-unfitted-level-3) 1pt solid;
|
|
|
color: var(--sklearn-color-unfitted-level-3);
|
|
|
}
|
|
|
|
|
|
.sk-estimator-doc-link.fitted,
|
|
|
a:link.sk-estimator-doc-link.fitted,
|
|
|
a:visited.sk-estimator-doc-link.fitted {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
border: var(--sklearn-color-fitted-level-3) 1pt solid;
|
|
|
color: var(--sklearn-color-fitted-level-3);
|
|
|
}
|
|
|
|
|
|
/* On hover */
|
|
|
div.sk-estimator:hover .sk-estimator-doc-link:hover,
|
|
|
.sk-estimator-doc-link:hover,
|
|
|
div.sk-label-container:hover .sk-estimator-doc-link:hover,
|
|
|
.sk-estimator-doc-link:hover {
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-3);
|
|
|
border: var(--sklearn-color-fitted-level-0) 1pt solid;
|
|
|
color: var(--sklearn-color-unfitted-level-0);
|
|
|
text-decoration: none;
|
|
|
}
|
|
|
|
|
|
div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
|
|
|
.sk-estimator-doc-link.fitted:hover,
|
|
|
div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
|
|
|
.sk-estimator-doc-link.fitted:hover {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-3);
|
|
|
border: var(--sklearn-color-fitted-level-0) 1pt solid;
|
|
|
color: var(--sklearn-color-fitted-level-0);
|
|
|
text-decoration: none;
|
|
|
}
|
|
|
|
|
|
/* Span, style for the box shown on hovering the info icon */
|
|
|
.sk-estimator-doc-link span {
|
|
|
display: none;
|
|
|
z-index: 9999;
|
|
|
position: relative;
|
|
|
font-weight: normal;
|
|
|
right: .2ex;
|
|
|
padding: .5ex;
|
|
|
margin: .5ex;
|
|
|
width: min-content;
|
|
|
min-width: 20ex;
|
|
|
max-width: 50ex;
|
|
|
color: var(--sklearn-color-text);
|
|
|
box-shadow: 2pt 2pt 4pt #999;
|
|
|
/* unfitted */
|
|
|
background: var(--sklearn-color-unfitted-level-0);
|
|
|
border: .5pt solid var(--sklearn-color-unfitted-level-3);
|
|
|
}
|
|
|
|
|
|
.sk-estimator-doc-link.fitted span {
|
|
|
/* fitted */
|
|
|
background: var(--sklearn-color-fitted-level-0);
|
|
|
border: var(--sklearn-color-fitted-level-3);
|
|
|
}
|
|
|
|
|
|
.sk-estimator-doc-link:hover span {
|
|
|
display: block;
|
|
|
}
|
|
|
|
|
|
/* "?"-specific style due to the `<a>` HTML tag */
|
|
|
|
|
|
.sk-global a.estimator_doc_link {
|
|
|
float: right;
|
|
|
font-size: 1rem;
|
|
|
line-height: 1em;
|
|
|
font-family: monospace;
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
border-radius: 1rem;
|
|
|
height: 1rem;
|
|
|
width: 1rem;
|
|
|
text-decoration: none;
|
|
|
/* unfitted */
|
|
|
color: var(--sklearn-color-unfitted-level-1);
|
|
|
border: var(--sklearn-color-unfitted-level-1) 1pt solid;
|
|
|
}
|
|
|
|
|
|
.sk-global a.estimator_doc_link.fitted {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
border: var(--sklearn-color-fitted-level-1) 1pt solid;
|
|
|
color: var(--sklearn-color-fitted-level-1);
|
|
|
}
|
|
|
|
|
|
/* On hover */
|
|
|
.sk-global a.estimator_doc_link:hover {
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-3);
|
|
|
color: var(--sklearn-color-background);
|
|
|
text-decoration: none;
|
|
|
}
|
|
|
|
|
|
.sk-global a.estimator_doc_link.fitted:hover {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-3);
|
|
|
}
|
|
|
|
|
|
.sk-top-container.sk-global {
|
|
|
/* pydata-sphinx-theme hides overflow, so scrolling is disabled.
|
|
|
We need to set it to !important and add tabindex="0" in the HTML
|
|
|
to allow keyboard-only users to navigate the display. */
|
|
|
overflow-x: scroll !important;
|
|
|
max-width: 100%;
|
|
|
}
|
|
|
|
|
|
.estimator-table {
|
|
|
font-family: monospace;
|
|
|
}
|
|
|
|
|
|
.estimator-table summary {
|
|
|
padding: .5rem;
|
|
|
cursor: pointer;
|
|
|
}
|
|
|
|
|
|
.estimator-table summary::marker {
|
|
|
font-size: 0.7rem;
|
|
|
}
|
|
|
|
|
|
.estimator-table details[open] {
|
|
|
padding-left: 0.1rem;
|
|
|
padding-right: 0.1rem;
|
|
|
padding-bottom: 0.3rem;
|
|
|
}
|
|
|
|
|
|
.estimator-table .parameters-table {
|
|
|
margin-left: auto !important;
|
|
|
margin-right: auto !important;
|
|
|
margin-top: 0;
|
|
|
}
|
|
|
|
|
|
.estimator-table .parameters-table tr:nth-child(odd) {
|
|
|
background-color: #fff;
|
|
|
}
|
|
|
|
|
|
.estimator-table .parameters-table tr:nth-child(even) {
|
|
|
background-color: #f6f6f6;
|
|
|
}
|
|
|
|
|
|
.estimator-table .parameters-table tr:hover td {
|
|
|
background-color: #e0e0e0;
|
|
|
}
|
|
|
|
|
|
.estimator-table table :is(td, th) {
|
|
|
border: 1px solid rgba(106, 105, 104, 0.232);
|
|
|
}
|
|
|
|
|
|
/*
|
|
|
`table td`is set in notebook with right text-align.
|
|
|
We need to overwrite it.
|
|
|
*/
|
|
|
.estimator-table table td.param {
|
|
|
text-align: left;
|
|
|
position: relative;
|
|
|
padding: 0;
|
|
|
}
|
|
|
|
|
|
.user-set td {
|
|
|
color:rgb(255, 94, 0);
|
|
|
text-align: left !important;
|
|
|
}
|
|
|
|
|
|
.user-set td.value {
|
|
|
color:rgb(255, 94, 0);
|
|
|
background-color: transparent;
|
|
|
}
|
|
|
|
|
|
.default td, .estimator-table th {
|
|
|
color: black;
|
|
|
text-align: left !important;
|
|
|
}
|
|
|
|
|
|
.user-set td i,
|
|
|
.default td i {
|
|
|
color: black;
|
|
|
}
|
|
|
|
|
|
td.fitted-att-type {
|
|
|
white-space: preserve nowrap;
|
|
|
}
|
|
|
|
|
|
/*
|
|
|
Styles for parameter documentation links
|
|
|
We need styling for visited so jupyter doesn't overwrite it
|
|
|
*/
|
|
|
a.param-doc-link,
|
|
|
a.param-doc-link:link,
|
|
|
a.param-doc-link:visited {
|
|
|
text-decoration: underline dashed;
|
|
|
text-underline-offset: .3em;
|
|
|
color: inherit;
|
|
|
display: block;
|
|
|
padding: .5em;
|
|
|
}
|
|
|
|
|
|
@supports(anchor-name: --doc-link) {
|
|
|
a.param-doc-link,
|
|
|
a.param-doc-link:link,
|
|
|
a.param-doc-link:visited {
|
|
|
anchor-name: --doc-link;
|
|
|
}
|
|
|
}
|
|
|
|
|
|
/* "hack" to make the entire area of the cell containing the link clickable */
|
|
|
a.param-doc-link::before {
|
|
|
position: absolute;
|
|
|
content: "";
|
|
|
inset: 0;
|
|
|
}
|
|
|
|
|
|
.param-doc-description {
|
|
|
display: none;
|
|
|
position: absolute;
|
|
|
z-index: 9999;
|
|
|
left: 0;
|
|
|
padding: .5ex;
|
|
|
margin-left: 1.5em;
|
|
|
color: var(--sklearn-color-text);
|
|
|
box-shadow: .3em .3em .4em #999;
|
|
|
width: max-content;
|
|
|
text-align: left;
|
|
|
max-height: 10em;
|
|
|
overflow-y: auto;
|
|
|
|
|
|
/* unfitted */
|
|
|
background: var(--sklearn-color-unfitted-level-0);
|
|
|
border: thin solid var(--sklearn-color-unfitted-level-3);
|
|
|
}
|
|
|
|
|
|
@supports(position-area: center right) {
|
|
|
.param-doc-description {
|
|
|
position-area: center right;
|
|
|
position: fixed;
|
|
|
margin-left: 0;
|
|
|
}
|
|
|
}
|
|
|
|
|
|
/* Fitted state for parameter tooltips */
|
|
|
.fitted .param-doc-description {
|
|
|
/* fitted */
|
|
|
background: var(--sklearn-color-fitted-level-0);
|
|
|
border: thin solid var(--sklearn-color-fitted-level-3);
|
|
|
}
|
|
|
|
|
|
.param-doc-link:hover .param-doc-description {
|
|
|
display: block;
|
|
|
}
|
|
|
|
|
|
.copy-paste-icon {
|
|
|
background-image: url(data:image/svg+xml;base64,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);
|
|
|
background-repeat: no-repeat;
|
|
|
background-size: 14px 14px;
|
|
|
background-position: 0;
|
|
|
display: inline-block;
|
|
|
width: 14px;
|
|
|
height: 14px;
|
|
|
cursor: pointer;
|
|
|
}
|
|
|
|
|
|
.features {
|
|
|
font-family: monospace;
|
|
|
cursor: pointer;
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
border: 1px dotted var(--sklearn-color-border-box);
|
|
|
border-radius: .20em;
|
|
|
margin-bottom: 0.5em;
|
|
|
font-size: inherit; /* Needed for jupyter */
|
|
|
}
|
|
|
|
|
|
.features.fitted {
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
}
|
|
|
|
|
|
.features summary {
|
|
|
cursor: pointer;
|
|
|
display: flex;
|
|
|
margin-bottom: 0;
|
|
|
text-align: center;
|
|
|
align-items: center;
|
|
|
justify-content: center;
|
|
|
gap: 0.5em;
|
|
|
padding: .25em;
|
|
|
}
|
|
|
|
|
|
.features details[open] > summary {
|
|
|
color: var(--sklearn-color-text);
|
|
|
background-color: var(--sklearn-color-unfitted-level-2);
|
|
|
border-radius: .20em 0 0 0;
|
|
|
}
|
|
|
|
|
|
.features.fitted details[open] > summary {
|
|
|
background-color: var(--sklearn-color-fitted-level-2);
|
|
|
border-radius: .20em 0 0 0;
|
|
|
}
|
|
|
|
|
|
.features details > summary .arrow::before {
|
|
|
content: "▸";
|
|
|
color: grey;
|
|
|
}
|
|
|
|
|
|
.features details[open] > summary .arrow::before {
|
|
|
content: "▾";
|
|
|
}
|
|
|
|
|
|
.features details:hover > summary {
|
|
|
margin: 0;
|
|
|
background-color: var(--sklearn-color-unfitted-level-2);
|
|
|
}
|
|
|
|
|
|
.features.fitted details:hover > summary {
|
|
|
margin: 0;
|
|
|
background-color: var(--sklearn-color-fitted-level-2);
|
|
|
}
|
|
|
|
|
|
.features .features-container {
|
|
|
max-width: 15em;
|
|
|
max-height: 10em;
|
|
|
overflow: auto;
|
|
|
scrollbar-width: thin;
|
|
|
padding: .25em 0.1rem;
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
border-radius: 0 0 .5em .5em;
|
|
|
}
|
|
|
|
|
|
.features.fitted .features-container {
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
}
|
|
|
|
|
|
.features .image-container {
|
|
|
block-size: 1em;
|
|
|
inline-size: 1em;
|
|
|
padding: 0;
|
|
|
margin: 0%;
|
|
|
display: flex;
|
|
|
justify-content: center;
|
|
|
align-items: center;
|
|
|
}
|
|
|
|
|
|
.features .copy-paste-icon {
|
|
|
background-size: 1em 1em;
|
|
|
width: 1em;
|
|
|
height: 1em;
|
|
|
filter: grayscale(100%) opacity(60%);
|
|
|
}
|
|
|
|
|
|
.features .features-container table {
|
|
|
width: 100%;
|
|
|
margin: 0.01em;
|
|
|
}
|
|
|
|
|
|
.features .features-container table tr:nth-child(odd) {
|
|
|
background-color: #fff;
|
|
|
}
|
|
|
|
|
|
.features .features-container table tr:nth-child(even) {
|
|
|
background-color: #f6f6f6;
|
|
|
}
|
|
|
|
|
|
.features .features-container table tr:hover {
|
|
|
background-color: #e0e0e0;
|
|
|
}
|
|
|
|
|
|
.features .features-container table {
|
|
|
table-layout: inherit;
|
|
|
}
|
|
|
|
|
|
.features .features-container table td {
|
|
|
text-align: left;
|
|
|
padding: 0 0.5em;
|
|
|
border: 1px solid rgba(106, 105, 104, 0.232);
|
|
|
white-space: nowrap;
|
|
|
color: var(--sklearn-color-text);
|
|
|
}
|
|
|
|
|
|
.total_features {
|
|
|
display: flex;
|
|
|
justify-content: center;
|
|
|
margin-top: 0.5em;
|
|
|
}
|
|
|
</style><body><div id="sk-container-id-4" tabindex="0" class="sk-top-container sk-global"><div class="sk-text-repr-fallback"><pre>LogisticRegression(C=100, random_state=22, solver='newton-cg', tol=1e-05)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually sk-global" id="sk-estimator-id-4" type="checkbox" checked><label for="sk-estimator-id-4" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted" data-param-prefix="">
|
|
|
<div class="estimator-table">
|
|
|
<details>
|
|
|
<summary>Parameters</summary>
|
|
|
<table class="parameters-table">
|
|
|
<tbody>
|
|
|
|
|
|
<tr class="user-set">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('C',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-C;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0">
|
|
|
C
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-C;">
|
|
|
C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">100</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="user-set">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('tol',
|
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this.parentElement.nextElementSibling)"
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></i></td>
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<td class="param">
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|
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<a class="param-doc-link"
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|
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style="anchor-name: --doc-link-tol;"
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|
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rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4">
|
|
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tol
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-tol;">
|
|
|
tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">1e-05</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="user-set">
|
|
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<td><i class="copy-paste-icon"
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onclick="copyToClipboard('random_state',
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|
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this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-random_state;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone">
|
|
|
random_state
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-random_state;">
|
|
|
random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">22</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="user-set">
|
|
|
<td><i class="copy-paste-icon"
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|
|
onclick="copyToClipboard('solver',
|
|
|
this.parentElement.nextElementSibling)"
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|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-solver;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27">
|
|
|
solver
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-solver;">
|
|
|
solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'}, default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br> class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br> 'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br> error.<br>- 'newton-cholesky' is a good choice for<br> `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br> categorical features with rare categories. Be aware that the memory usage<br> of this solver has a quadratic dependency on `n_features * n_classes`<br> because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br> and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br> one-versus-rest scheme for the multiclass setting one can wrap it with the<br> :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br> The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br> for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br> Elastic-Net) and on (multinomial) multiclass support:<br><br> ================= ======================== ======================<br> solver l1_ratio multinomial multiclass<br> ================= ======================== ======================<br> 'lbfgs' l1_ratio=0 yes<br> 'liblinear' l1_ratio=1 or l1_ratio=0 no<br> 'newton-cg' l1_ratio=0 yes<br> 'newton-cholesky' l1_ratio=0 yes<br> 'sag' l1_ratio=0 yes<br> 'saga' 0<=l1_ratio<=1 yes<br> ================= ======================== ======================<br><br>.. note::<br> 'sag' and 'saga' fast convergence is only guaranteed on features<br> with approximately the same scale. You can preprocess the data with<br> a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br> Refer to the :ref:`User Guide <Logistic_regression>` for more<br> information regarding :class:`LogisticRegression` and more specifically the<br> :ref:`Table <logistic_regression_solvers>`<br> summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br> Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br> version 0.18.<br>.. versionadded:: 0.19<br> SAGA solver.<br>.. versionchanged:: 0.22<br> The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br> newton-cholesky solver. Multinomial support in version 1.6.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">'newton-cg'</td>
|
|
|
</tr>
|
|
|
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|
|
|
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|
<tr class="default">
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|
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<td><i class="copy-paste-icon"
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|
onclick="copyToClipboard('penalty',
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|
|
this.parentElement.nextElementSibling)"
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|
|
></i></td>
|
|
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<td class="param">
|
|
|
<a class="param-doc-link"
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|
|
style="anchor-name: --doc-link-penalty;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27">
|
|
|
penalty
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-penalty;">
|
|
|
penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add an L2 penalty term and it is the default choice;<br>- `'l1'`: add an L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br> Some penalties may not work with some solvers. See the parameter<br> `solver` below, to know the compatibility between the penalty and<br> solver.<br><br>.. versionadded:: 0.19<br> l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br> `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br> Use `l1_ratio` and `C` instead. `l1_ratio=0` for `penalty='l2'`,<br> `l1_ratio=1` for `penalty='l1'`, `l1_ratio` set to any float between 0 and 1<br> for `penalty='elasticnet'`, and `C=np.inf` for `penalty=None`.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">'deprecated'</td>
|
|
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</tr>
|
|
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|
|
|
|
|
<tr class="default">
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|
|
<td><i class="copy-paste-icon"
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|
|
onclick="copyToClipboard('l1_ratio',
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this.parentElement.nextElementSibling)"
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|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-l1_ratio;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0">
|
|
|
l1_ratio
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-l1_ratio;">
|
|
|
l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br> Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br> solvers. See the parameter `solver` below, to know the compatibility between<br> the penalty and solver.<br><br>.. versionchanged:: 1.8<br> Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br> `None` is deprecated and will be removed in version 1.10. Always use<br> `l1_ratio` to specify the penalty type.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">0.0</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
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|
|
onclick="copyToClipboard('dual',
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|
|
this.parentElement.nextElementSibling)"
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|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-dual;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse">
|
|
|
dual
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-dual;">
|
|
|
dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">False</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
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|
|
onclick="copyToClipboard('fit_intercept',
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|
|
this.parentElement.nextElementSibling)"
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|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-fit_intercept;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue">
|
|
|
fit_intercept
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-fit_intercept;">
|
|
|
fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">True</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
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|
onclick="copyToClipboard('intercept_scaling',
|
|
|
this.parentElement.nextElementSibling)"
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|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-intercept_scaling;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1">
|
|
|
intercept_scaling
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-intercept_scaling;">
|
|
|
intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a "synthetic" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br> The synthetic feature weight is subject to L1 or L2<br> regularization as all other features.<br> To lessen the effect of regularization on synthetic feature weight<br> (and therefore on the intercept) `intercept_scaling` has to be increased.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">1</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('class_weight',
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|
|
this.parentElement.nextElementSibling)"
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|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-class_weight;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone">
|
|
|
class_weight
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-class_weight;">
|
|
|
class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The "balanced" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br> *class_weight='balanced'*</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">None</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
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|
onclick="copyToClipboard('max_iter',
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|
this.parentElement.nextElementSibling)"
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|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-max_iter;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100">
|
|
|
max_iter
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-max_iter;">
|
|
|
max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">100</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('verbose',
|
|
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this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-verbose;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0">
|
|
|
verbose
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-verbose;">
|
|
|
verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">0</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
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|
onclick="copyToClipboard('warm_start',
|
|
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this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-warm_start;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse">
|
|
|
warm_start
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-warm_start;">
|
|
|
warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br> *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">False</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('n_jobs',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-n_jobs;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone">
|
|
|
n_jobs
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-n_jobs;">
|
|
|
n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br> `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">None</td>
|
|
|
</tr>
|
|
|
|
|
|
</tbody>
|
|
|
</table>
|
|
|
</details>
|
|
|
</div>
|
|
|
|
|
|
<div class="estimator-table">
|
|
|
<details>
|
|
|
<summary>Fitted attributes</summary>
|
|
|
<table class="parameters-table">
|
|
|
<tbody>
|
|
|
<tr>
|
|
|
<th>Name</th>
|
|
|
<th>Type</th>
|
|
|
<th>Value</th>
|
|
|
</tr>
|
|
|
|
|
|
<tr class="default">
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-classes_;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%20%29">
|
|
|
classes_
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-classes_;">
|
|
|
classes_: ndarray of shape (n_classes, )<br><br>A list of class labels known to the classifier.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="fitted-att-type">ndarray[int64](2,)</td>
|
|
|
<td>[0,1]</td>
|
|
|
|
|
|
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-coef_;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=coef_,-ndarray%20or%20CSR%20matrix%20of%20shape%20%281%2C%20n_features%29%20or%20%28n_classes%2C%20n_features%29">
|
|
|
coef_
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-coef_;">
|
|
|
coef_: ndarray or CSR matrix of shape (1, n_features) or (n_classes, n_features)<br><br>Coefficients of the features in the decision function.<br><br>`coef_` is of shape (1, n_features) when the given problem is binary.<br><br>By default, it will be created as a dense array, but can be turned to<br>sparse (CSR format) through :meth:`sparsify` (which can be beneficial<br>under L1 regularization when many coefficients are zero), and back to<br>dense through :meth:`densify`.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="fitted-att-type">ndarray[float64](1, 2)</td>
|
|
|
<td>[[ 2.6 ,-0.74]]</td>
|
|
|
|
|
|
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-intercept_;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20or%20%28n_classes%2C%29">
|
|
|
intercept_
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-intercept_;">
|
|
|
intercept_: ndarray of shape (1,) or (n_classes,)<br><br>Intercept (a.k.a. bias) added to the decision function.<br><br>If `fit_intercept` is set to False, the intercept is set to zero.<br>`intercept_` is of shape (1,) when the given problem is binary.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="fitted-att-type">ndarray[float64](1,)</td>
|
|
|
<td>[-14.16]</td>
|
|
|
|
|
|
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-n_features_in_;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_features_in_,-int">
|
|
|
n_features_in_
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-n_features_in_;">
|
|
|
n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="fitted-att-type">int</td>
|
|
|
<td>2</td>
|
|
|
|
|
|
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-n_iter_;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_iter_,-ndarray%20of%20shape%20%281%2C%20%29">
|
|
|
n_iter_
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-n_iter_;">
|
|
|
n_iter_: ndarray of shape (1, )<br><br>Actual number of iterations for all classes.<br><br>.. versionchanged:: 0.20<br><br> In SciPy <= 1.0.0 the number of lbfgs iterations may exceed<br> ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="fitted-att-type">ndarray[int32](1,)</td>
|
|
|
<td>[11]</td>
|
|
|
|
|
|
|
|
|
</tr>
|
|
|
|
|
|
</tbody>
|
|
|
</table>
|
|
|
</details>
|
|
|
</div>
|
|
|
</div></div></div></div></div><script>/* Authors: The scikit-learn developers
|
|
|
SPDX-License-Identifier: BSD-3-Clause
|
|
|
*/
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|
|
function copyToClipboard(text, element) {
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|
|
// Get the parameter prefix from the closest toggleable content
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|
|
const toggleableContent = element.closest('.sk-toggleable__content');
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const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';
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const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;
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const originalStyle = element.style;
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const computedStyle = window.getComputedStyle(element);
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const originalWidth = computedStyle.width;
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const originalHTML = element.innerHTML.replace('Copied!', '');
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navigator.clipboard.writeText(fullParamName)
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.then(() => {
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element.style.width = originalWidth;
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element.style.color = 'green';
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element.innerHTML = "Copied!";
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setTimeout(() => {
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element.innerHTML = originalHTML;
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element.style = originalStyle;
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}, 2000);
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})
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.catch(err => {
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console.error('Failed to copy:', err);
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element.style.color = 'red';
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element.innerHTML = "Failed!";
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setTimeout(() => {
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element.innerHTML = originalHTML;
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element.style = originalStyle;
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}, 2000);
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});
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return false;
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}
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document.querySelectorAll('.copy-paste-icon').forEach(function(element) {
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const toggleableContent = element.closest('.sk-toggleable__content');
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const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';
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const parent = element.parentElement;
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if (!parent || !parent.nextElementSibling) {
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console.warn('Expected copy-paste icon is missing from the DOM structure');
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return;
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}
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const paramName = element.parentElement.nextElementSibling
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.textContent.trim().split(' ')[0];
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const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;
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|
element.setAttribute('title', fullParamName);
|
|
|
});
|
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|
|
/**
|
|
|
* Copy the list of feature names formatted as a Python list.
|
|
|
*
|
|
|
* @param {HTMLElement} element - The copy button inside a `.features` block; its siblings
|
|
|
* contain a `details` element and a table containing feature named.
|
|
|
* @returns {boolean} Always returns `false` so callers can prevent the default click behavior.
|
|
|
*/
|
|
|
function copyFeatureNamesToClipboard(element) {
|
|
|
var detailsElem = element.closest('.features').querySelector('details');
|
|
|
var wasOpen = detailsElem.open;
|
|
|
detailsElem.open = true;
|
|
|
var content = element.closest('.features').querySelector('tbody')
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|
|
.innerText.trim();
|
|
|
if (!wasOpen) detailsElem.open = false;
|
|
|
const rows = content.split('\n').map(row => ` "${row}"`);
|
|
|
const formattedText = `[\n${rows.join(',\n')},\n]`;
|
|
|
const originalHTML = element.innerHTML.replace('✔', '');
|
|
|
const originalStyle = element.style;
|
|
|
const copyMark = document.createElement('span');
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|
copyMark.innerHTML = '✔';
|
|
|
copyMark.style.color = 'blue';
|
|
|
copyMark.style.fontSize = '1em';
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|
|
navigator.clipboard.writeText(formattedText)
|
|
|
.then(() => {
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element.style.display = 'none';
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element.parentElement.appendChild(copyMark);
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setTimeout(() => {
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copyMark.remove();
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element.innerHTML = originalHTML;
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|
|
element.style = originalStyle;
|
|
|
}, 1000);
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|
})
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|
.catch(err => {
|
|
|
console.error('Failed to copy:', err);
|
|
|
element.style.color = 'orange';
|
|
|
element.innerHTML = "Failed!";
|
|
|
setTimeout(() => {
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|
|
element.innerHTML = originalHTML;
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|
|
element.style = originalStyle;
|
|
|
}, 1000);
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});
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return false;
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|
}
|
|
|
/**
|
|
|
* Adapted from Skrub
|
|
|
* https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789
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|
|
* @returns "light" or "dark"
|
|
|
*/
|
|
|
function detectTheme(element) {
|
|
|
const body = document.querySelector('body');
|
|
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|
|
// Check VSCode theme
|
|
|
const themeKindAttr = body.getAttribute('data-vscode-theme-kind');
|
|
|
const themeNameAttr = body.getAttribute('data-vscode-theme-name');
|
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|
|
|
if (themeKindAttr && themeNameAttr) {
|
|
|
const themeKind = themeKindAttr.toLowerCase();
|
|
|
const themeName = themeNameAttr.toLowerCase();
|
|
|
|
|
|
if (themeKind.includes("dark") || themeName.includes("dark")) {
|
|
|
return "dark";
|
|
|
}
|
|
|
if (themeKind.includes("light") || themeName.includes("light")) {
|
|
|
return "light";
|
|
|
}
|
|
|
}
|
|
|
|
|
|
// Check Jupyter theme
|
|
|
if (body.getAttribute('data-jp-theme-light') === 'false') {
|
|
|
return 'dark';
|
|
|
} else if (body.getAttribute('data-jp-theme-light') === 'true') {
|
|
|
return 'light';
|
|
|
}
|
|
|
|
|
|
// Guess based on a parent element's color
|
|
|
const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');
|
|
|
const match = color.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)\s*$/i);
|
|
|
if (match) {
|
|
|
const [r, g, b] = [
|
|
|
parseFloat(match[1]),
|
|
|
parseFloat(match[2]),
|
|
|
parseFloat(match[3])
|
|
|
];
|
|
|
|
|
|
// https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness
|
|
|
const luma = 0.299 * r + 0.587 * g + 0.114 * b;
|
|
|
|
|
|
if (luma > 180) {
|
|
|
// If the text is very bright we have a dark theme
|
|
|
return 'dark';
|
|
|
}
|
|
|
if (luma < 75) {
|
|
|
// If the text is very dark we have a light theme
|
|
|
return 'light';
|
|
|
}
|
|
|
// Otherwise fall back to the next heuristic.
|
|
|
}
|
|
|
|
|
|
// Fallback to system preference
|
|
|
return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';
|
|
|
}
|
|
|
|
|
|
|
|
|
function forceTheme(elementId) {
|
|
|
const estimatorElement = document.querySelector(`#${elementId}`);
|
|
|
if (estimatorElement === null) {
|
|
|
console.error(`Element with id ${elementId} not found.`);
|
|
|
} else {
|
|
|
const theme = detectTheme(estimatorElement);
|
|
|
estimatorElement.classList.add(theme);
|
|
|
}
|
|
|
}
|
|
|
|
|
|
forceTheme('sk-container-id-4');</script></body>
|
|
|
|
|
|
|
|
|
|
|
|
#### Coordinate Grid Generation & Probability Mapping
|
|
|
|
|
|
This block builds the mathematical testing grid used to map out the complete probability landscape:
|
|
|
|
|
|
* **`np.meshgrid`**: Generates a dense $100 \times 100$ coordinate grid across the sepal feature space (Length: 3–8 cm, Width: 0–6 cm).
|
|
|
* **`Xnew` (`np.c_`)**: Flattens and couples the grid matrices into a matrix of 10,000 discrete 2D spatial coordinates.
|
|
|
* **`predict_proba(Xnew)`**: Evaluates the trained model across the entire grid, computing the continuous probabilities needed to render the decision contours and 3D surfaces.
|
|
|
|
|
|
|
|
|
```python
|
|
|
x0, x1 = np.meshgrid(
|
|
|
np.linspace(3,8,100).reshape(-1,1),
|
|
|
np.linspace(0,6,100).reshape(-1,1)
|
|
|
)
|
|
|
Xnew = np.c_[x0.ravel(), x1.ravel()]
|
|
|
yPred = mylrvir.predict_proba(Xnew)
|
|
|
```
|
|
|
|
|
|
|
|
|
```python
|
|
|
plt.figure(figsize=(10,4))
|
|
|
plt.plot(X[y==0,0], X[y==0,1],'bs',label='No Virg')
|
|
|
plt.plot(X[y==1,0], X[y==1,1],'g^',label='Virginica')
|
|
|
zz=yPred[:,1].reshape(x0.shape)
|
|
|
contour=plt.contour(x0,x1,zz)
|
|
|
plt.clabel(contour, inline=1,fontsize=15)
|
|
|
plt.xlabel("Sepal Length")
|
|
|
plt.ylabel("Sepal Width")
|
|
|
plt.legend()
|
|
|
plt.show()
|
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
This plot visualizes the continuous probability space generated by the trained bivariate model:
|
|
|
|
|
|
* **Sample Distribution:** Blue squares represent non-virginica samples ($y=0$), and green triangles represent *Iris virginica* ($y=1$) mapped across Sepal Length and Sepal Width.
|
|
|
* **Probability Contours (`plt.contour`):** The labeled contour lines map specific probability thresholds. They show how the model's prediction confidence transitions across the 2D space.
|
|
|
* **Decision Boundary:** The contour line labeled **0.5** marks the exact geometric threshold. Any sample falling past this line is classified as *Iris virginica*, capturing the spatial trade-off between both sepal measurements.
|
|
|
|
|
|
|
|
|
```python
|
|
|
fig, ax =plt.subplots(subplot_kw={"projection": "3d"})
|
|
|
surf = ax.plot_surface(x0,x1,zz, cmap='jet')
|
|
|
ax.scatter(iris.data[:,0:1], iris.data[:,1:2], y, 'or')
|
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x2a3c5a946e0>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
This cell projects the bivariate logistic regression model into a 3D coordinate space to visualize the complete probability landscape:
|
|
|
|
|
|
* **Axis Dimensions:** The horizontal axes represent **Sepal Length** ($x_1$) and **Sepal Width** ($x_2$), while the vertical axis ($Z$) tracks the continuous model probability $\sigma(z) \in [0, 1]$.
|
|
|
* **Probability Surface (`plot_surface`):** The Sigmoid function is rendered as a 3D sheet using the `jet` colormap. It displays the non-linear S-curve transition dynamically stretched across the two-dimensional feature plane.
|
|
|
* **True Labels Spatial Scatter (`ax.scatter`):** The red markers plot the actual samples at their exact spatial coordinates and true binary height ($z = 1$ for *Iris virginica*, $z = 0$ for others). This highlights how the optimized surface splits the space to fit the data points.
|
|
|
|
|
|
### Modelo 5: Multiple features and muticlass classifier
|
|
|
|
|
|
#### Multi-Feature & Multi-Class Model Training
|
|
|
|
|
|
This cell configures and trains the final baseline model to handle all three species simultaneously within a two-dimensional feature space:
|
|
|
|
|
|
* **Bivariate Inputs (`X`):** Slices index `[:, 0:2]` to utilize both **Sepal Length** and **Sepal Width** as the predictor variables.
|
|
|
* **Multi-Class Target (`y`):** Retains the original multi-class target labels (`0`, `1`, `2`) without applying binarization, expanding the task from a single boundary to a three-class decision space.
|
|
|
* **`LogisticRegression(C=100, solver='lbfgs')`:** Trains a multinomial classifier. The algorithm optimizes a distinct set of weights and biases for each target category, preparing the model to partition the 2D plane into three distinct classification zones.
|
|
|
|
|
|
|
|
|
```python
|
|
|
X = iris.data[:,0:2]
|
|
|
y = iris.target
|
|
|
lrmc = LogisticRegression(
|
|
|
solver='lbfgs',
|
|
|
C=100,
|
|
|
random_state=22
|
|
|
)
|
|
|
lrmc.fit(X,y)
|
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
<style>.sk-global {
|
|
|
/* Definition of color scheme common for light and dark mode */
|
|
|
--sklearn-color-text: #000;
|
|
|
--sklearn-color-text-muted: #666;
|
|
|
--sklearn-color-line: gray;
|
|
|
/* Definition of color scheme for unfitted estimators */
|
|
|
--sklearn-color-unfitted-level-0: #fff5e6;
|
|
|
--sklearn-color-unfitted-level-1: #f6e4d2;
|
|
|
--sklearn-color-unfitted-level-2: #ffe0b3;
|
|
|
--sklearn-color-unfitted-level-3: chocolate;
|
|
|
/* Definition of color scheme for fitted estimators */
|
|
|
--sklearn-color-fitted-level-0: #f0f8ff;
|
|
|
--sklearn-color-fitted-level-1: #d4ebff;
|
|
|
--sklearn-color-fitted-level-2: #b3dbfd;
|
|
|
--sklearn-color-fitted-level-3: cornflowerblue;
|
|
|
}
|
|
|
|
|
|
.sk-global.light {
|
|
|
/* Specific color for light theme */
|
|
|
--sklearn-color-text-on-default-background: black;
|
|
|
--sklearn-color-background: white;
|
|
|
--sklearn-color-border-box: black;
|
|
|
--sklearn-color-icon: #696969;
|
|
|
}
|
|
|
|
|
|
.sk-global.dark {
|
|
|
--sklearn-color-text-on-default-background: white;
|
|
|
--sklearn-color-background: #111;
|
|
|
--sklearn-color-border-box: white;
|
|
|
--sklearn-color-icon: #878787;
|
|
|
}
|
|
|
|
|
|
.sk-global {
|
|
|
color: var(--sklearn-color-text);
|
|
|
}
|
|
|
|
|
|
.sk-global pre {
|
|
|
padding: 0;
|
|
|
}
|
|
|
|
|
|
.sk-global input.sk-hidden--visually {
|
|
|
border: 0;
|
|
|
clip-path: inset(100%);
|
|
|
height: 1px;
|
|
|
margin: -1px;
|
|
|
overflow: hidden;
|
|
|
padding: 0;
|
|
|
position: absolute;
|
|
|
width: 1px;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-dashed-wrapped {
|
|
|
border: 1px dashed var(--sklearn-color-line);
|
|
|
margin: 0 0.4em 0.5em 0.4em;
|
|
|
box-sizing: border-box;
|
|
|
padding-bottom: 0.4em;
|
|
|
background-color: var(--sklearn-color-background);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-container {
|
|
|
/* jupyter's `normalize.less` sets `[hidden] { display: none; }`
|
|
|
but bootstrap.min.css set `[hidden] { display: none !important; }`
|
|
|
so we also need the `!important` here to be able to override the
|
|
|
default hidden behavior on the sphinx rendered scikit-learn.org.
|
|
|
See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
|
|
|
display: inline-block !important;
|
|
|
position: relative;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-text-repr-fallback {
|
|
|
display: none;
|
|
|
}
|
|
|
|
|
|
div.sk-parallel-item,
|
|
|
div.sk-serial,
|
|
|
div.sk-item {
|
|
|
/* draw centered vertical line to link estimators */
|
|
|
background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
|
|
|
background-size: 2px 100%;
|
|
|
background-repeat: no-repeat;
|
|
|
background-position: center center;
|
|
|
}
|
|
|
|
|
|
/* Parallel-specific style estimator block */
|
|
|
|
|
|
.sk-global div.sk-parallel-item::after {
|
|
|
content: "";
|
|
|
width: 100%;
|
|
|
border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
|
|
|
flex-grow: 1;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-parallel {
|
|
|
display: flex;
|
|
|
align-items: stretch;
|
|
|
justify-content: center;
|
|
|
background-color: var(--sklearn-color-background);
|
|
|
position: relative;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-parallel-item {
|
|
|
display: flex;
|
|
|
flex-direction: column;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-parallel-item:first-child::after {
|
|
|
align-self: flex-end;
|
|
|
width: 50%;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-parallel-item:last-child::after {
|
|
|
align-self: flex-start;
|
|
|
width: 50%;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-parallel-item:only-child::after {
|
|
|
width: 0;
|
|
|
}
|
|
|
|
|
|
/* Serial-specific style estimator block */
|
|
|
|
|
|
.sk-global div.sk-serial {
|
|
|
display: flex;
|
|
|
flex-direction: column;
|
|
|
align-items: center;
|
|
|
background-color: var(--sklearn-color-background);
|
|
|
padding-right: 1em;
|
|
|
padding-left: 1em;
|
|
|
}
|
|
|
|
|
|
|
|
|
/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
|
|
|
clickable and can be expanded/collapsed.
|
|
|
- Pipeline and ColumnTransformer use this feature and define the default style
|
|
|
- Estimators will overwrite some part of the style using the `sk-estimator` class
|
|
|
*/
|
|
|
|
|
|
/* Pipeline and ColumnTransformer style (default) */
|
|
|
|
|
|
.sk-global div.sk-toggleable {
|
|
|
/* Default theme specific background. It is overwritten whether we have a
|
|
|
specific estimator or a Pipeline/ColumnTransformer */
|
|
|
background-color: var(--sklearn-color-background);
|
|
|
}
|
|
|
|
|
|
/* Toggleable label */
|
|
|
.sk-global label.sk-toggleable__label {
|
|
|
cursor: pointer;
|
|
|
display: flex;
|
|
|
width: 100%;
|
|
|
margin-bottom: 0;
|
|
|
padding: 0.5em;
|
|
|
box-sizing: border-box;
|
|
|
text-align: center;
|
|
|
align-items: center;
|
|
|
justify-content: center;
|
|
|
gap: 0.5em;
|
|
|
}
|
|
|
|
|
|
.sk-global label.sk-toggleable__label .caption {
|
|
|
font-size: 0.6rem;
|
|
|
font-weight: lighter;
|
|
|
color: var(--sklearn-color-text-muted);
|
|
|
}
|
|
|
|
|
|
.sk-global label.sk-toggleable__label-arrow:before {
|
|
|
/* Arrow on the left of the label */
|
|
|
content: "▸";
|
|
|
float: left;
|
|
|
margin-right: 0.25em;
|
|
|
color: var(--sklearn-color-icon);
|
|
|
}
|
|
|
|
|
|
.sk-global label.sk-toggleable__label-arrow:hover:before {
|
|
|
color: var(--sklearn-color-text);
|
|
|
}
|
|
|
|
|
|
/* Toggleable content - dropdown */
|
|
|
|
|
|
.sk-global div.sk-toggleable__content {
|
|
|
display: none;
|
|
|
text-align: left;
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-toggleable__content.fitted {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-toggleable__content pre {
|
|
|
margin: 0.2em;
|
|
|
border-radius: 0.25em;
|
|
|
color: var(--sklearn-color-text);
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-toggleable__content.fitted pre {
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
}
|
|
|
|
|
|
.sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {
|
|
|
/* Expand drop-down */
|
|
|
display: block;
|
|
|
width: 100%;
|
|
|
overflow: visible;
|
|
|
}
|
|
|
|
|
|
.sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
|
|
|
content: "▾";
|
|
|
}
|
|
|
|
|
|
/* Pipeline/ColumnTransformer-specific style */
|
|
|
|
|
|
.sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
|
|
color: var(--sklearn-color-text);
|
|
|
background-color: var(--sklearn-color-unfitted-level-2);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
|
|
background-color: var(--sklearn-color-fitted-level-2);
|
|
|
}
|
|
|
|
|
|
/* Estimator-specific style */
|
|
|
|
|
|
/* Colorize estimator box */
|
|
|
.sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-2);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-2);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-label label.sk-toggleable__label,
|
|
|
.sk-global div.sk-label label {
|
|
|
/* The background is the default theme color */
|
|
|
color: var(--sklearn-color-text-on-default-background);
|
|
|
}
|
|
|
|
|
|
/* On hover, darken the color of the background */
|
|
|
.sk-global div.sk-label:hover label.sk-toggleable__label {
|
|
|
color: var(--sklearn-color-text);
|
|
|
background-color: var(--sklearn-color-unfitted-level-2);
|
|
|
}
|
|
|
|
|
|
/* Label box, darken color on hover, fitted */
|
|
|
.sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
|
|
|
color: var(--sklearn-color-text);
|
|
|
background-color: var(--sklearn-color-fitted-level-2);
|
|
|
}
|
|
|
|
|
|
/* Estimator label */
|
|
|
|
|
|
.sk-global div.sk-label label {
|
|
|
font-family: monospace;
|
|
|
font-weight: bold;
|
|
|
line-height: 1.2em;
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-label-container {
|
|
|
text-align: center;
|
|
|
}
|
|
|
|
|
|
/* Estimator-specific */
|
|
|
.sk-global div.sk-estimator {
|
|
|
font-family: monospace;
|
|
|
border: 1px dotted var(--sklearn-color-border-box);
|
|
|
border-radius: 0.25em;
|
|
|
box-sizing: border-box;
|
|
|
margin-bottom: 0.5em;
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-estimator.fitted {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
}
|
|
|
|
|
|
/* on hover */
|
|
|
.sk-global div.sk-estimator:hover {
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-2);
|
|
|
}
|
|
|
|
|
|
.sk-global div.sk-estimator.fitted:hover {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-2);
|
|
|
}
|
|
|
|
|
|
/* Specification for estimator info (e.g. "i" and "?") */
|
|
|
|
|
|
/* Common style for "i" and "?" */
|
|
|
|
|
|
.sk-estimator-doc-link,
|
|
|
a:link.sk-estimator-doc-link,
|
|
|
a:visited.sk-estimator-doc-link {
|
|
|
float: right;
|
|
|
font-size: smaller;
|
|
|
line-height: 1em;
|
|
|
font-family: monospace;
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
border-radius: 1em;
|
|
|
height: 1em;
|
|
|
width: 1em;
|
|
|
text-decoration: none !important;
|
|
|
margin-left: 0.5em;
|
|
|
text-align: center;
|
|
|
/* unfitted */
|
|
|
border: var(--sklearn-color-unfitted-level-3) 1pt solid;
|
|
|
color: var(--sklearn-color-unfitted-level-3);
|
|
|
}
|
|
|
|
|
|
.sk-estimator-doc-link.fitted,
|
|
|
a:link.sk-estimator-doc-link.fitted,
|
|
|
a:visited.sk-estimator-doc-link.fitted {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
border: var(--sklearn-color-fitted-level-3) 1pt solid;
|
|
|
color: var(--sklearn-color-fitted-level-3);
|
|
|
}
|
|
|
|
|
|
/* On hover */
|
|
|
div.sk-estimator:hover .sk-estimator-doc-link:hover,
|
|
|
.sk-estimator-doc-link:hover,
|
|
|
div.sk-label-container:hover .sk-estimator-doc-link:hover,
|
|
|
.sk-estimator-doc-link:hover {
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-3);
|
|
|
border: var(--sklearn-color-fitted-level-0) 1pt solid;
|
|
|
color: var(--sklearn-color-unfitted-level-0);
|
|
|
text-decoration: none;
|
|
|
}
|
|
|
|
|
|
div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
|
|
|
.sk-estimator-doc-link.fitted:hover,
|
|
|
div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
|
|
|
.sk-estimator-doc-link.fitted:hover {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-3);
|
|
|
border: var(--sklearn-color-fitted-level-0) 1pt solid;
|
|
|
color: var(--sklearn-color-fitted-level-0);
|
|
|
text-decoration: none;
|
|
|
}
|
|
|
|
|
|
/* Span, style for the box shown on hovering the info icon */
|
|
|
.sk-estimator-doc-link span {
|
|
|
display: none;
|
|
|
z-index: 9999;
|
|
|
position: relative;
|
|
|
font-weight: normal;
|
|
|
right: .2ex;
|
|
|
padding: .5ex;
|
|
|
margin: .5ex;
|
|
|
width: min-content;
|
|
|
min-width: 20ex;
|
|
|
max-width: 50ex;
|
|
|
color: var(--sklearn-color-text);
|
|
|
box-shadow: 2pt 2pt 4pt #999;
|
|
|
/* unfitted */
|
|
|
background: var(--sklearn-color-unfitted-level-0);
|
|
|
border: .5pt solid var(--sklearn-color-unfitted-level-3);
|
|
|
}
|
|
|
|
|
|
.sk-estimator-doc-link.fitted span {
|
|
|
/* fitted */
|
|
|
background: var(--sklearn-color-fitted-level-0);
|
|
|
border: var(--sklearn-color-fitted-level-3);
|
|
|
}
|
|
|
|
|
|
.sk-estimator-doc-link:hover span {
|
|
|
display: block;
|
|
|
}
|
|
|
|
|
|
/* "?"-specific style due to the `<a>` HTML tag */
|
|
|
|
|
|
.sk-global a.estimator_doc_link {
|
|
|
float: right;
|
|
|
font-size: 1rem;
|
|
|
line-height: 1em;
|
|
|
font-family: monospace;
|
|
|
background-color: var(--sklearn-color-unfitted-level-0);
|
|
|
border-radius: 1rem;
|
|
|
height: 1rem;
|
|
|
width: 1rem;
|
|
|
text-decoration: none;
|
|
|
/* unfitted */
|
|
|
color: var(--sklearn-color-unfitted-level-1);
|
|
|
border: var(--sklearn-color-unfitted-level-1) 1pt solid;
|
|
|
}
|
|
|
|
|
|
.sk-global a.estimator_doc_link.fitted {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-0);
|
|
|
border: var(--sklearn-color-fitted-level-1) 1pt solid;
|
|
|
color: var(--sklearn-color-fitted-level-1);
|
|
|
}
|
|
|
|
|
|
/* On hover */
|
|
|
.sk-global a.estimator_doc_link:hover {
|
|
|
/* unfitted */
|
|
|
background-color: var(--sklearn-color-unfitted-level-3);
|
|
|
color: var(--sklearn-color-background);
|
|
|
text-decoration: none;
|
|
|
}
|
|
|
|
|
|
.sk-global a.estimator_doc_link.fitted:hover {
|
|
|
/* fitted */
|
|
|
background-color: var(--sklearn-color-fitted-level-3);
|
|
|
}
|
|
|
|
|
|
.sk-top-container.sk-global {
|
|
|
/* pydata-sphinx-theme hides overflow, so scrolling is disabled.
|
|
|
We need to set it to !important and add tabindex="0" in the HTML
|
|
|
to allow keyboard-only users to navigate the display. */
|
|
|
overflow-x: scroll !important;
|
|
|
max-width: 100%;
|
|
|
}
|
|
|
|
|
|
.estimator-table {
|
|
|
font-family: monospace;
|
|
|
}
|
|
|
|
|
|
.estimator-table summary {
|
|
|
padding: .5rem;
|
|
|
cursor: pointer;
|
|
|
}
|
|
|
|
|
|
.estimator-table summary::marker {
|
|
|
font-size: 0.7rem;
|
|
|
}
|
|
|
|
|
|
.estimator-table details[open] {
|
|
|
padding-left: 0.1rem;
|
|
|
padding-right: 0.1rem;
|
|
|
padding-bottom: 0.3rem;
|
|
|
}
|
|
|
|
|
|
.estimator-table .parameters-table {
|
|
|
margin-left: auto !important;
|
|
|
margin-right: auto !important;
|
|
|
margin-top: 0;
|
|
|
}
|
|
|
|
|
|
.estimator-table .parameters-table tr:nth-child(odd) {
|
|
|
background-color: #fff;
|
|
|
}
|
|
|
|
|
|
.estimator-table .parameters-table tr:nth-child(even) {
|
|
|
background-color: #f6f6f6;
|
|
|
}
|
|
|
|
|
|
.estimator-table .parameters-table tr:hover td {
|
|
|
background-color: #e0e0e0;
|
|
|
}
|
|
|
|
|
|
.estimator-table table :is(td, th) {
|
|
|
border: 1px solid rgba(106, 105, 104, 0.232);
|
|
|
}
|
|
|
|
|
|
/*
|
|
|
`table td`is set in notebook with right text-align.
|
|
|
We need to overwrite it.
|
|
|
*/
|
|
|
.estimator-table table td.param {
|
|
|
text-align: left;
|
|
|
position: relative;
|
|
|
padding: 0;
|
|
|
}
|
|
|
|
|
|
.user-set td {
|
|
|
color:rgb(255, 94, 0);
|
|
|
text-align: left !important;
|
|
|
}
|
|
|
|
|
|
.user-set td.value {
|
|
|
color:rgb(255, 94, 0);
|
|
|
background-color: transparent;
|
|
|
}
|
|
|
|
|
|
.default td, .estimator-table th {
|
|
|
color: black;
|
|
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text-align: left !important;
|
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|
}
|
|
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.user-set td i,
|
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.default td i {
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color: black;
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}
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td.fitted-att-type {
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white-space: preserve nowrap;
|
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}
|
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/*
|
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Styles for parameter documentation links
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We need styling for visited so jupyter doesn't overwrite it
|
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*/
|
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a.param-doc-link,
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a.param-doc-link:link,
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a.param-doc-link:visited {
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text-decoration: underline dashed;
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text-underline-offset: .3em;
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color: inherit;
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padding: .5em;
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}
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@supports(anchor-name: --doc-link) {
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a.param-doc-link,
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a.param-doc-link:link,
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a.param-doc-link:visited {
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anchor-name: --doc-link;
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}
|
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}
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/* "hack" to make the entire area of the cell containing the link clickable */
|
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a.param-doc-link::before {
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position: absolute;
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content: "";
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inset: 0;
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|
}
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.param-doc-description {
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|
display: none;
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position: absolute;
|
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z-index: 9999;
|
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left: 0;
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padding: .5ex;
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|
margin-left: 1.5em;
|
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color: var(--sklearn-color-text);
|
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box-shadow: .3em .3em .4em #999;
|
|
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width: max-content;
|
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text-align: left;
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max-height: 10em;
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overflow-y: auto;
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/* unfitted */
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background: var(--sklearn-color-unfitted-level-0);
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border: thin solid var(--sklearn-color-unfitted-level-3);
|
|
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}
|
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@supports(position-area: center right) {
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|
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.param-doc-description {
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position-area: center right;
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position: fixed;
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margin-left: 0;
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}
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}
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/* Fitted state for parameter tooltips */
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.fitted .param-doc-description {
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/* fitted */
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background: var(--sklearn-color-fitted-level-0);
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border: thin solid var(--sklearn-color-fitted-level-3);
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}
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.param-doc-link:hover .param-doc-description {
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display: block;
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}
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.copy-paste-icon {
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background-image: url(data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCA0NDggNTEyIj48IS0tIUZvbnQgQXdlc29tZSBGcmVlIDYuNy4yIGJ5IEBmb250YXdlc29tZSAtIGh0dHBzOi8vZm9udGF3ZXNvbWUuY29tIExpY2Vuc2UgLSBodHRwczovL2ZvbnRhd2Vzb21lLmNvbS9saWNlbnNlL2ZyZWUgQ29weXJpZ2h0IDIwMjUgRm9udGljb25zLCBJbmMuLS0+PHBhdGggZD0iTTIwOCAwTDMzMi4xIDBjMTIuNyAwIDI0LjkgNS4xIDMzLjkgMTQuMWw2Ny45IDY3LjljOSA5IDE0LjEgMjEuMiAxNC4xIDMzLjlMNDQ4IDMzNmMwIDI2LjUtMjEuNSA0OC00OCA0OGwtMTkyIDBjLTI2LjUgMC00OC0yMS41LTQ4LTQ4bDAtMjg4YzAtMjYuNSAyMS41LTQ4IDQ4LTQ4ek00OCAxMjhsODAgMCAwIDY0LTY0IDAgMCAyNTYgMTkyIDAgMC0zMiA2NCAwIDAgNDhjMCAyNi41LTIxLjUgNDgtNDggNDhMNDggNTEyYy0yNi41IDAtNDgtMjEuNS00OC00OEwwIDE3NmMwLTI2LjUgMjEuNS00OCA0OC00OHoiLz48L3N2Zz4=);
|
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background-repeat: no-repeat;
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background-size: 14px 14px;
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background-position: 0;
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display: inline-block;
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width: 14px;
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height: 14px;
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cursor: pointer;
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}
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.features {
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font-family: monospace;
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cursor: pointer;
|
|
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background-color: var(--sklearn-color-unfitted-level-0);
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border: 1px dotted var(--sklearn-color-border-box);
|
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border-radius: .20em;
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margin-bottom: 0.5em;
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font-size: inherit; /* Needed for jupyter */
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}
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.features.fitted {
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background-color: var(--sklearn-color-fitted-level-0);
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}
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.features summary {
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cursor: pointer;
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display: flex;
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margin-bottom: 0;
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text-align: center;
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align-items: center;
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justify-content: center;
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|
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gap: 0.5em;
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padding: .25em;
|
|
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}
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.features details[open] > summary {
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color: var(--sklearn-color-text);
|
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background-color: var(--sklearn-color-unfitted-level-2);
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border-radius: .20em 0 0 0;
|
|
|
}
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.features.fitted details[open] > summary {
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background-color: var(--sklearn-color-fitted-level-2);
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border-radius: .20em 0 0 0;
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}
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.features details > summary .arrow::before {
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content: "▸";
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color: grey;
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}
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.features details[open] > summary .arrow::before {
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content: "▾";
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}
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.features details:hover > summary {
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|
|
margin: 0;
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|
|
background-color: var(--sklearn-color-unfitted-level-2);
|
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}
|
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|
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|
|
.features.fitted details:hover > summary {
|
|
|
margin: 0;
|
|
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background-color: var(--sklearn-color-fitted-level-2);
|
|
|
}
|
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.features .features-container {
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|
|
max-width: 15em;
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max-height: 10em;
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overflow: auto;
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scrollbar-width: thin;
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padding: .25em 0.1rem;
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background-color: var(--sklearn-color-unfitted-level-0);
|
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border-radius: 0 0 .5em .5em;
|
|
|
}
|
|
|
|
|
|
.features.fitted .features-container {
|
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|
background-color: var(--sklearn-color-fitted-level-0);
|
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}
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.features .image-container {
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block-size: 1em;
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inline-size: 1em;
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padding: 0;
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margin: 0%;
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display: flex;
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justify-content: center;
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align-items: center;
|
|
|
}
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.features .copy-paste-icon {
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background-size: 1em 1em;
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width: 1em;
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height: 1em;
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filter: grayscale(100%) opacity(60%);
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}
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.features .features-container table {
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|
|
width: 100%;
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margin: 0.01em;
|
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|
}
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.features .features-container table tr:nth-child(odd) {
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background-color: #fff;
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}
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.features .features-container table tr:nth-child(even) {
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background-color: #f6f6f6;
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|
}
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.features .features-container table tr:hover {
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background-color: #e0e0e0;
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}
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.features .features-container table {
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table-layout: inherit;
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}
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.features .features-container table td {
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text-align: left;
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padding: 0 0.5em;
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border: 1px solid rgba(106, 105, 104, 0.232);
|
|
|
white-space: nowrap;
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|
|
color: var(--sklearn-color-text);
|
|
|
}
|
|
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|
|
|
.total_features {
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|
display: flex;
|
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justify-content: center;
|
|
|
margin-top: 0.5em;
|
|
|
}
|
|
|
</style><body><div id="sk-container-id-5" tabindex="0" class="sk-top-container sk-global"><div class="sk-text-repr-fallback"><pre>LogisticRegression(C=100, random_state=22)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually sk-global" id="sk-estimator-id-5" type="checkbox" checked><label for="sk-estimator-id-5" class="sk-toggleable__label fitted sk-toggleable__label-arrow"><div><div>LogisticRegression</div></div><div><a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html">?<span>Documentation for LogisticRegression</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></div></label><div class="sk-toggleable__content fitted" data-param-prefix="">
|
|
|
<div class="estimator-table">
|
|
|
<details>
|
|
|
<summary>Parameters</summary>
|
|
|
<table class="parameters-table">
|
|
|
<tbody>
|
|
|
|
|
|
<tr class="user-set">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('C',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-C;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=C,-float%2C%20default%3D1.0">
|
|
|
C
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-C;">
|
|
|
C: float, default=1.0<br><br>Inverse of regularization strength; must be a positive float.<br>Like in support vector machines, smaller values specify stronger<br>regularization. `C=np.inf` results in unpenalized logistic regression.<br>For a visual example on the effect of tuning the `C` parameter<br>with an L1 penalty, see:<br>:ref:`sphx_glr_auto_examples_linear_model_plot_logistic_path.py`.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">100</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="user-set">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('random_state',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-random_state;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=random_state,-int%2C%20RandomState%20instance%2C%20default%3DNone">
|
|
|
random_state
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-random_state;">
|
|
|
random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the<br>data. See :term:`Glossary <random_state>` for details.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">22</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('penalty',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-penalty;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=penalty,-%7B%27l1%27%2C%20%27l2%27%2C%20%27elasticnet%27%2C%20None%7D%2C%20default%3D%27l2%27">
|
|
|
penalty
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-penalty;">
|
|
|
penalty: {'l1', 'l2', 'elasticnet', None}, default='l2'<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `'l2'`: add an L2 penalty term and it is the default choice;<br>- `'l1'`: add an L1 penalty term;<br>- `'elasticnet'`: both L1 and L2 penalty terms are added.<br><br>.. warning::<br> Some penalties may not work with some solvers. See the parameter<br> `solver` below, to know the compatibility between the penalty and<br> solver.<br><br>.. versionadded:: 0.19<br> l1 penalty with SAGA solver (allowing 'multinomial' + L1)<br><br>.. deprecated:: 1.8<br> `penalty` was deprecated in version 1.8 and will be removed in 1.10.<br> Use `l1_ratio` and `C` instead. `l1_ratio=0` for `penalty='l2'`,<br> `l1_ratio=1` for `penalty='l1'`, `l1_ratio` set to any float between 0 and 1<br> for `penalty='elasticnet'`, and `C=np.inf` for `penalty=None`.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">'deprecated'</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('l1_ratio',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-l1_ratio;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=l1_ratio,-float%2C%20default%3D0.0">
|
|
|
l1_ratio
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-l1_ratio;">
|
|
|
l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 <= l1_ratio <= 1`. Setting<br>`l1_ratio=1` gives a pure L1-penalty, setting `l1_ratio=0` a pure L2-penalty.<br>Any value between 0 and 1 gives an Elastic-Net penalty of the form<br>`l1_ratio * L1 + (1 - l1_ratio) * L2`.<br><br>.. warning::<br> Certain values of `l1_ratio`, i.e. some penalties, may not work with some<br> solvers. See the parameter `solver` below, to know the compatibility between<br> the penalty and solver.<br><br>.. versionchanged:: 1.8<br> Default value changed from None to 0.0.<br><br>.. deprecated:: 1.8<br> `None` is deprecated and will be removed in version 1.10. Always use<br> `l1_ratio` to specify the penalty type.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">0.0</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('dual',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-dual;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse">
|
|
|
dual
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-dual;">
|
|
|
dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation <regularized-logistic-loss>`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples > n_features.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">False</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('tol',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-tol;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=tol,-float%2C%20default%3D1e-4">
|
|
|
tol
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-tol;">
|
|
|
tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">0.0001</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('fit_intercept',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-fit_intercept;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=fit_intercept,-bool%2C%20default%3DTrue">
|
|
|
fit_intercept
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-fit_intercept;">
|
|
|
fit_intercept: bool, default=True<br><br>Specifies if a constant (a.k.a. bias or intercept) should be<br>added to the decision function.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">True</td>
|
|
|
</tr>
|
|
|
|
|
|
|
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<tr class="default">
|
|
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<td><i class="copy-paste-icon"
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|
onclick="copyToClipboard('intercept_scaling',
|
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|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
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|
style="anchor-name: --doc-link-intercept_scaling;"
|
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|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_scaling,-float%2C%20default%3D1">
|
|
|
intercept_scaling
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-intercept_scaling;">
|
|
|
intercept_scaling: float, default=1<br><br>Useful only when the solver `liblinear` is used<br>and `self.fit_intercept` is set to `True`. In this case, `x` becomes<br>`[x, self.intercept_scaling]`,<br>i.e. a "synthetic" feature with constant value equal to<br>`intercept_scaling` is appended to the instance vector.<br>The intercept becomes<br>``intercept_scaling * synthetic_feature_weight``.<br><br>.. note::<br> The synthetic feature weight is subject to L1 or L2<br> regularization as all other features.<br> To lessen the effect of regularization on synthetic feature weight<br> (and therefore on the intercept) `intercept_scaling` has to be increased.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">1</td>
|
|
|
</tr>
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|
|
|
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<tr class="default">
|
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<td><i class="copy-paste-icon"
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|
onclick="copyToClipboard('class_weight',
|
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|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-class_weight;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=class_weight,-dict%20or%20%27balanced%27%2C%20default%3DNone">
|
|
|
class_weight
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-class_weight;">
|
|
|
class_weight: dict or 'balanced', default=None<br><br>Weights associated with classes in the form ``{class_label: weight}``.<br>If not given, all classes are supposed to have weight one.<br><br>The "balanced" mode uses the values of y to automatically adjust<br>weights inversely proportional to class frequencies in the input data<br>as ``n_samples / (n_classes * np.bincount(y))``.<br><br>Note that these weights will be multiplied with sample_weight (passed<br>through the fit method) if sample_weight is specified.<br><br>.. versionadded:: 0.17<br> *class_weight='balanced'*</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">None</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('solver',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-solver;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=solver,-%7B%27lbfgs%27%2C%20%27liblinear%27%2C%20%27newton-cg%27%2C%20%27newton-cholesky%27%2C%20%27sag%27%2C%20%27saga%27%7D%2C%20%20%20%20%20%20%20%20%20%20%20%20%20default%3D%27lbfgs%27">
|
|
|
solver
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-solver;">
|
|
|
solver: {'lbfgs', 'liblinear', 'newton-cg', 'newton-cholesky', 'sag', 'saga'}, default='lbfgs'<br><br>Algorithm to use in the optimization problem. Default is 'lbfgs'.<br>To choose a solver, you might want to consider the following aspects:<br><br>- 'lbfgs' is a good default solver because it works reasonably well for a wide<br> class of problems.<br>- For :term:`multiclass` problems (`n_classes >= 3`), all solvers except<br> 'liblinear' minimize the full multinomial loss, 'liblinear' will raise an<br> error.<br>- 'newton-cholesky' is a good choice for<br> `n_samples` >> `n_features * n_classes`, especially with one-hot encoded<br> categorical features with rare categories. Be aware that the memory usage<br> of this solver has a quadratic dependency on `n_features * n_classes`<br> because it explicitly computes the full Hessian matrix.<br>- For small datasets, 'liblinear' is a good choice, whereas 'sag'<br> and 'saga' are faster for large ones;<br>- 'liblinear' can only handle binary classification by default. To apply a<br> one-versus-rest scheme for the multiclass setting one can wrap it with the<br> :class:`~sklearn.multiclass.OneVsRestClassifier`.<br><br>.. warning::<br> The choice of the algorithm depends on the penalty chosen (`l1_ratio=0`<br> for L2-penalty, `l1_ratio=1` for L1-penalty and `0 < l1_ratio < 1` for<br> Elastic-Net) and on (multinomial) multiclass support:<br><br> ================= ======================== ======================<br> solver l1_ratio multinomial multiclass<br> ================= ======================== ======================<br> 'lbfgs' l1_ratio=0 yes<br> 'liblinear' l1_ratio=1 or l1_ratio=0 no<br> 'newton-cg' l1_ratio=0 yes<br> 'newton-cholesky' l1_ratio=0 yes<br> 'sag' l1_ratio=0 yes<br> 'saga' 0<=l1_ratio<=1 yes<br> ================= ======================== ======================<br><br>.. note::<br> 'sag' and 'saga' fast convergence is only guaranteed on features<br> with approximately the same scale. You can preprocess the data with<br> a scaler from :mod:`sklearn.preprocessing`.<br><br>.. seealso::<br> Refer to the :ref:`User Guide <Logistic_regression>` for more<br> information regarding :class:`LogisticRegression` and more specifically the<br> :ref:`Table <logistic_regression_solvers>`<br> summarizing solver/penalty supports.<br><br>.. versionadded:: 0.17<br> Stochastic Average Gradient (SAG) descent solver. Multinomial support in<br> version 0.18.<br>.. versionadded:: 0.19<br> SAGA solver.<br>.. versionchanged:: 0.22<br> The default solver changed from 'liblinear' to 'lbfgs' in 0.22.<br>.. versionadded:: 1.2<br> newton-cholesky solver. Multinomial support in version 1.6.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">'lbfgs'</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('max_iter',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-max_iter;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=max_iter,-int%2C%20default%3D100">
|
|
|
max_iter
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-max_iter;">
|
|
|
max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">100</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('verbose',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-verbose;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0">
|
|
|
verbose
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-verbose;">
|
|
|
verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">0</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('warm_start',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-warm_start;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=warm_start,-bool%2C%20default%3DFalse">
|
|
|
warm_start
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-warm_start;">
|
|
|
warm_start: bool, default=False<br><br>When set to True, reuse the solution of the previous call to fit as<br>initialization, otherwise, just erase the previous solution.<br>Useless for liblinear solver. See :term:`the Glossary <warm_start>`.<br><br>.. versionadded:: 0.17<br> *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">False</td>
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td><i class="copy-paste-icon"
|
|
|
onclick="copyToClipboard('n_jobs',
|
|
|
this.parentElement.nextElementSibling)"
|
|
|
></i></td>
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-n_jobs;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_jobs,-int%2C%20default%3DNone">
|
|
|
n_jobs
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-n_jobs;">
|
|
|
n_jobs: int, default=None<br><br>Does not have any effect.<br><br>.. deprecated:: 1.8<br> `n_jobs` is deprecated in version 1.8 and will be removed in 1.10.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="value">None</td>
|
|
|
</tr>
|
|
|
|
|
|
</tbody>
|
|
|
</table>
|
|
|
</details>
|
|
|
</div>
|
|
|
|
|
|
<div class="estimator-table">
|
|
|
<details>
|
|
|
<summary>Fitted attributes</summary>
|
|
|
<table class="parameters-table">
|
|
|
<tbody>
|
|
|
<tr>
|
|
|
<th>Name</th>
|
|
|
<th>Type</th>
|
|
|
<th>Value</th>
|
|
|
</tr>
|
|
|
|
|
|
<tr class="default">
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-classes_;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=classes_,-ndarray%20of%20shape%20%28n_classes%2C%20%29">
|
|
|
classes_
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-classes_;">
|
|
|
classes_: ndarray of shape (n_classes, )<br><br>A list of class labels known to the classifier.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="fitted-att-type">ndarray[int64](3,)</td>
|
|
|
<td>[0,1,2]</td>
|
|
|
|
|
|
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-coef_;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=coef_,-ndarray%20or%20CSR%20matrix%20of%20shape%20%281%2C%20n_features%29%20or%20%28n_classes%2C%20n_features%29">
|
|
|
coef_
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-coef_;">
|
|
|
coef_: ndarray or CSR matrix of shape (1, n_features) or (n_classes, n_features)<br><br>Coefficients of the features in the decision function.<br><br>`coef_` is of shape (1, n_features) when the given problem is binary.<br><br>By default, it will be created as a dense array, but can be turned to<br>sparse (CSR format) through :meth:`sparsify` (which can be beneficial<br>under L1 regularization when many coefficients are zero), and back to<br>dense through :meth:`densify`.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="fitted-att-type">ndarray[float64](3, 2)</td>
|
|
|
<td>[[-9.33, 8.18],
|
|
|
[ 3.73,-4.3 ],
|
|
|
[ 5.61,-3.88]]</td>
|
|
|
|
|
|
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-intercept_;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=intercept_,-ndarray%20of%20shape%20%281%2C%29%20or%20%28n_classes%2C%29">
|
|
|
intercept_
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-intercept_;">
|
|
|
intercept_: ndarray of shape (1,) or (n_classes,)<br><br>Intercept (a.k.a. bias) added to the decision function.<br><br>If `fit_intercept` is set to False, the intercept is set to zero.<br>`intercept_` is of shape (1,) when the given problem is binary.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="fitted-att-type">ndarray[float64](3,)</td>
|
|
|
<td>[ 25.2 , -6.11,-19.1 ]</td>
|
|
|
|
|
|
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-n_features_in_;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_features_in_,-int">
|
|
|
n_features_in_
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-n_features_in_;">
|
|
|
n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="fitted-att-type">int</td>
|
|
|
<td>2</td>
|
|
|
|
|
|
|
|
|
</tr>
|
|
|
|
|
|
|
|
|
<tr class="default">
|
|
|
<td class="param">
|
|
|
<a class="param-doc-link"
|
|
|
style="anchor-name: --doc-link-n_iter_;"
|
|
|
rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_iter_,-ndarray%20of%20shape%20%281%2C%20%29">
|
|
|
n_iter_
|
|
|
<span class="param-doc-description"
|
|
|
style="position-anchor: --doc-link-n_iter_;">
|
|
|
n_iter_: ndarray of shape (1, )<br><br>Actual number of iterations for all classes.<br><br>.. versionchanged:: 0.20<br><br> In SciPy <= 1.0.0 the number of lbfgs iterations may exceed<br> ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.</span>
|
|
|
</a>
|
|
|
</td>
|
|
|
<td class="fitted-att-type">ndarray[int32](1,)</td>
|
|
|
<td>[59]</td>
|
|
|
|
|
|
|
|
|
</tr>
|
|
|
|
|
|
</tbody>
|
|
|
</table>
|
|
|
</details>
|
|
|
</div>
|
|
|
</div></div></div></div></div><script>/* Authors: The scikit-learn developers
|
|
|
SPDX-License-Identifier: BSD-3-Clause
|
|
|
*/
|
|
|
|
|
|
function copyToClipboard(text, element) {
|
|
|
// Get the parameter prefix from the closest toggleable content
|
|
|
const toggleableContent = element.closest('.sk-toggleable__content');
|
|
|
const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';
|
|
|
const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;
|
|
|
|
|
|
const originalStyle = element.style;
|
|
|
const computedStyle = window.getComputedStyle(element);
|
|
|
const originalWidth = computedStyle.width;
|
|
|
const originalHTML = element.innerHTML.replace('Copied!', '');
|
|
|
|
|
|
navigator.clipboard.writeText(fullParamName)
|
|
|
.then(() => {
|
|
|
element.style.width = originalWidth;
|
|
|
element.style.color = 'green';
|
|
|
element.innerHTML = "Copied!";
|
|
|
|
|
|
setTimeout(() => {
|
|
|
element.innerHTML = originalHTML;
|
|
|
element.style = originalStyle;
|
|
|
}, 2000);
|
|
|
})
|
|
|
.catch(err => {
|
|
|
console.error('Failed to copy:', err);
|
|
|
element.style.color = 'red';
|
|
|
element.innerHTML = "Failed!";
|
|
|
setTimeout(() => {
|
|
|
element.innerHTML = originalHTML;
|
|
|
element.style = originalStyle;
|
|
|
}, 2000);
|
|
|
});
|
|
|
return false;
|
|
|
}
|
|
|
|
|
|
document.querySelectorAll('.copy-paste-icon').forEach(function(element) {
|
|
|
const toggleableContent = element.closest('.sk-toggleable__content');
|
|
|
const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';
|
|
|
|
|
|
const parent = element.parentElement;
|
|
|
if (!parent || !parent.nextElementSibling) {
|
|
|
console.warn('Expected copy-paste icon is missing from the DOM structure');
|
|
|
return;
|
|
|
}
|
|
|
|
|
|
const paramName = element.parentElement.nextElementSibling
|
|
|
.textContent.trim().split(' ')[0];
|
|
|
const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;
|
|
|
|
|
|
element.setAttribute('title', fullParamName);
|
|
|
});
|
|
|
|
|
|
/**
|
|
|
* Copy the list of feature names formatted as a Python list.
|
|
|
*
|
|
|
* @param {HTMLElement} element - The copy button inside a `.features` block; its siblings
|
|
|
* contain a `details` element and a table containing feature named.
|
|
|
* @returns {boolean} Always returns `false` so callers can prevent the default click behavior.
|
|
|
*/
|
|
|
function copyFeatureNamesToClipboard(element) {
|
|
|
var detailsElem = element.closest('.features').querySelector('details');
|
|
|
var wasOpen = detailsElem.open;
|
|
|
detailsElem.open = true;
|
|
|
var content = element.closest('.features').querySelector('tbody')
|
|
|
.innerText.trim();
|
|
|
if (!wasOpen) detailsElem.open = false;
|
|
|
const rows = content.split('\n').map(row => ` "${row}"`);
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const formattedText = `[\n${rows.join(',\n')},\n]`;
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const originalHTML = element.innerHTML.replace('✔', '');
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const originalStyle = element.style;
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const copyMark = document.createElement('span');
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copyMark.innerHTML = '✔';
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copyMark.style.color = 'blue';
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copyMark.style.fontSize = '1em';
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navigator.clipboard.writeText(formattedText)
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.then(() => {
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element.style.display = 'none';
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element.parentElement.appendChild(copyMark);
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setTimeout(() => {
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copyMark.remove();
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element.innerHTML = originalHTML;
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element.style = originalStyle;
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}, 1000);
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})
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.catch(err => {
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|
console.error('Failed to copy:', err);
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element.style.color = 'orange';
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element.innerHTML = "Failed!";
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setTimeout(() => {
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element.innerHTML = originalHTML;
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element.style = originalStyle;
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}, 1000);
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});
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return false;
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}
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/**
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* Adapted from Skrub
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* https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789
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* @returns "light" or "dark"
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*/
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function detectTheme(element) {
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const body = document.querySelector('body');
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|
// Check VSCode theme
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|
const themeKindAttr = body.getAttribute('data-vscode-theme-kind');
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|
const themeNameAttr = body.getAttribute('data-vscode-theme-name');
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|
if (themeKindAttr && themeNameAttr) {
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|
const themeKind = themeKindAttr.toLowerCase();
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|
const themeName = themeNameAttr.toLowerCase();
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|
if (themeKind.includes("dark") || themeName.includes("dark")) {
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return "dark";
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}
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if (themeKind.includes("light") || themeName.includes("light")) {
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return "light";
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}
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}
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|
// Check Jupyter theme
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|
if (body.getAttribute('data-jp-theme-light') === 'false') {
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|
return 'dark';
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|
} else if (body.getAttribute('data-jp-theme-light') === 'true') {
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|
return 'light';
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|
}
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|
// Guess based on a parent element's color
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|
const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');
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|
const match = color.match(/^rgb\s*\(\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*\)\s*$/i);
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|
|
if (match) {
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|
|
const [r, g, b] = [
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|
|
parseFloat(match[1]),
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|
parseFloat(match[2]),
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|
|
parseFloat(match[3])
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|
|
];
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|
|
|
// https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness
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|
|
const luma = 0.299 * r + 0.587 * g + 0.114 * b;
|
|
|
|
|
|
if (luma > 180) {
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|
|
// If the text is very bright we have a dark theme
|
|
|
return 'dark';
|
|
|
}
|
|
|
if (luma < 75) {
|
|
|
// If the text is very dark we have a light theme
|
|
|
return 'light';
|
|
|
}
|
|
|
// Otherwise fall back to the next heuristic.
|
|
|
}
|
|
|
|
|
|
// Fallback to system preference
|
|
|
return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';
|
|
|
}
|
|
|
|
|
|
|
|
|
function forceTheme(elementId) {
|
|
|
const estimatorElement = document.querySelector(`#${elementId}`);
|
|
|
if (estimatorElement === null) {
|
|
|
console.error(`Element with id ${elementId} not found.`);
|
|
|
} else {
|
|
|
const theme = detectTheme(estimatorElement);
|
|
|
estimatorElement.classList.add(theme);
|
|
|
}
|
|
|
}
|
|
|
|
|
|
forceTheme('sk-container-id-5');</script></body>
|
|
|
|
|
|
|
|
|
|
|
|
#### Multi-Class Grid Generation and Probability Evaluation
|
|
|
|
|
|
This cell sets up the coordinate testing matrix to evaluate the multi-class prediction behavior across the entire sepal feature space:
|
|
|
|
|
|
* **`np.meshgrid`**: Constructs a dense $100 \times 100$ coordinate grid bounding the Sepal Length (3–8 cm) and Sepal Width (0–6 cm) ranges.
|
|
|
* **`Xnew` (`np.c_`)**: Flattens and pairs the grid elements into a matrix of 10,000 distinct 2D spatial coordinates.
|
|
|
* **`lrmc.predict_proba(Xnew)`**: Computes a three-column probability matrix for each point. This mapping determines the exact multi-class boundaries by evaluating the localized likelihood for *Setosa*, *Versicolor*, and *Virginica* simultaneously.
|
|
|
|
|
|
|
|
|
```python
|
|
|
x0, x1 = np.meshgrid(
|
|
|
np.linspace(3,8,100).reshape(-1,1),
|
|
|
np.linspace(0,6,100).reshape(-1,1)
|
|
|
)
|
|
|
Xnew = np.c_[x0.ravel(), x1.ravel()]
|
|
|
yPred = lrmc.predict_proba(Xnew)
|
|
|
```
|
|
|
|
|
|
|
|
|
```python
|
|
|
plt.figure(figsize=(10,4))
|
|
|
plt.plot(X[y==0,0], X[y==0,1],'.b',label='Setosa')
|
|
|
plt.plot(X[y==1,0], X[y==1,1],'+g',label='Versi')
|
|
|
plt.plot(X[y==2,0], X[y==2,1],'*m',label='Virgi')
|
|
|
zz=yPred[:,1].reshape(x0.shape)
|
|
|
contour=plt.contour(x0,x1,zz)
|
|
|
plt.clabel(contour, inline=1,fontsize=15)
|
|
|
plt.xlabel("Sepal Length")
|
|
|
plt.ylabel("Sepal Width")
|
|
|
plt.legend()
|
|
|
plt.show()
|
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
This plot maps the continuous probability distribution of the middle class within the multi-class decision space:
|
|
|
|
|
|
* **Three-Class Distribution:** Displays all species simultaneously using distinct markers: blue dots for *Setosa* ($y=0$), green pluses for *Versicolor* ($y=1$), and magenta stars for *Virginica* ($y=2$).
|
|
|
* **Target Class Extraction (`yPred[:, 1]`):** Slicing the second column of the probability matrix isolates and tracks the specific localized likelihood of a sample being **Iris versicolor**.
|
|
|
* **Localized Probability Ridge:** Unlike the linear boundaries seen in binary classification, the multinomial model creates a bounded peak or "ridge" to isolate the middle class. The highest contour line (**0.90**) tightly encapsulates the core *Versicolor* cluster, dropping off systematically as the features move toward *Setosa* (left) or *Virginica* (right) territories.
|
|
|
|
|
|
|
|
|
```python
|
|
|
yPred = lrmc.predict(Xnew)
|
|
|
plt.figure(figsize=(10,6))
|
|
|
plt.plot(X[y==0,0], X[y==0,1],'bs',label='Setosa')
|
|
|
plt.plot(X[y==1,0], X[y==1,1],'g^',label='Versi')
|
|
|
plt.plot(X[y==2,0], X[y==2,1],'*m',label='Virgi')
|
|
|
zz=yPred.reshape(x0.shape)
|
|
|
contour=plt.contourf(x0,x1,zz, cmap='jet', alpha=0.3)
|
|
|
plt.clabel(contour, inline=1,fontsize=15)
|
|
|
plt.xlabel("Sepal Length")
|
|
|
plt.ylabel("Sepal Width")
|
|
|
plt.legend()
|
|
|
plt.show()
|
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
This plot visualizes the ultimate classification boundaries by partitioning the entire 2D feature space into hard decision zones:
|
|
|
|
|
|
* **Hard Class Assignment (`lrmc.predict`):** Converts continuous probabilities into discrete class verdicts (`0`, `1`, or `2`) by applying an *argmax* function (selecting the class with the highest probability for each point).
|
|
|
* **Filled Decision Regions (`plt.contourf`):** Shades the coordinate plane into three distinct, solid zones using the `jet` colormap:
|
|
|
* **Blue Region:** Absolute classification space for *Iris setosa*.
|
|
|
* **Green Region:** Absolute classification space for *Iris versicolor*.
|
|
|
* **Red/Orange Region:** Absolute classification space for *Iris virginica*.
|
|
|
* **Boundary Analysis:** The sharp geometric intersections between the colored blocks define the definitive decision thresholds. This layout explicitly reveals how the linear multi-class model manages the regional trade-offs and handles the spatial overlap between the *Versicolor* and *Virginica* samples.
|
|
|
|
|
|
|
|
|
```python
|
|
|
fig, ax =plt.subplots(subplot_kw={"projection": "3d"})
|
|
|
surf = ax.plot_surface(x0,x1,zz, cmap='jet')
|
|
|
ax.scatter(iris.data[:,0:1], iris.data[:,1:2], y, 'or')
|
|
|
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x2a3c02451d0>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
This cell integrates the actual dataset samples into the 3D hard decision space to visually evaluate the multi-class model's accuracy:
|
|
|
|
|
|
* **Discrete Vertical Alignment ($Z$):** Both the staircase surface and the scatter markers use the integer multi-class taxonomy ($0$ for *Setosa*, $1$ for *Versicolor*, and $2$ for *Virginica*) instead of continuous probabilities.
|
|
|
* **Stepped Surface (`plot_surface`):** Represents the geometric boundaries computed by the model. Each level dictates the categorical verdict zone based on the combination of sepal features.
|
|
|
* **True Labels Scatter (`ax.scatter`):** Plots the real flower samples at their actual feature coordinates and true species height. This allows immediate visual verification of performance: samples resting on their matching colored step are correctly classified, while those caught on the wrong tier highlight the exact instances of classification error caused by spatial overlap.
|