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     ]
    }
   ],
   "source": [
    "!pip3 install scikit-learn\n",
    "!pip3 install matplotlib\n",
    "!pip3 install numpy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cd073961",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. _iris_dataset:\n",
      "\n",
      "Iris plants dataset\n",
      "--------------------\n",
      "\n",
      "**Data Set Characteristics:**\n",
      "\n",
      ":Number of Instances: 150 (50 in each of three classes)\n",
      ":Number of Attributes: 4 numeric, predictive attributes and the class\n",
      ":Attribute Information:\n",
      "    - sepal length in cm\n",
      "    - sepal width in cm\n",
      "    - petal length in cm\n",
      "    - petal width in cm\n",
      "    - class:\n",
      "            - Iris-Setosa\n",
      "            - Iris-Versicolour\n",
      "            - Iris-Virginica\n",
      "\n",
      ":Summary Statistics:\n",
      "\n",
      "============== ==== ==== ======= ===== ====================\n",
      "                Min  Max   Mean    SD   Class Correlation\n",
      "============== ==== ==== ======= ===== ====================\n",
      "sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
      "sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
      "petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
      "petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
      "============== ==== ==== ======= ===== ====================\n",
      "\n",
      ":Missing Attribute Values: None\n",
      ":Class Distribution: 33.3% for each of 3 classes.\n",
      ":Creator: R.A. Fisher\n",
      ":Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
      ":Date: July, 1988\n",
      "\n",
      "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
      "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
      "Machine Learning Repository, which has two wrong data points.\n",
      "\n",
      "This is perhaps the best known database to be found in the\n",
      "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
      "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
      "data set contains 3 classes of 50 instances each, where each class refers to a\n",
      "type of iris plant.  One class is linearly separable from the other 2; the\n",
      "latter are NOT linearly separable from each other.\n",
      "\n",
      ".. dropdown:: References\n",
      "\n",
      "  - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
      "    Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
      "    Mathematical Statistics\" (John Wiley, NY, 1950).\n",
      "  - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
      "    (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
      "  - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
      "    Structure and Classification Rule for Recognition in Partially Exposed\n",
      "    Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
      "    Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
      "  - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
      "    on Information Theory, May 1972, 431-433.\n",
      "  - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
      "    conceptual clustering system finds 3 classes in the data.\n",
      "  - Many, many more ...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "iris=datasets.load_iris()\n",
    "print(iris.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0ba60b35",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "958f8009",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sl = iris.data[:, 0:1]\n",
    "sw = iris.data[:, 1:2]\n",
    "plt.plot(sl,sw,'.k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "211fa51f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.target"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6bc04d7",
   "metadata": {},
   "source": [
    "# Decision Boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9e5198a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t= np.linspace(-10,10,100)\n",
    "sig = 1/(1+np.exp(-t))\n",
    "plt.plot(t,sig, '.b', label=r\"$\\sigma$\")\n",
    "plt.legend(loc='upper left', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f36a094",
   "metadata": {},
   "source": [
    "# Iris-Setosa Classifier based on petal width"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "004d1192",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = iris.data[:, 3:]\n",
    "y = (iris.target == 0).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1069ed1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.sk-global {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       ".sk-global.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       ".sk-global.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       ".sk-global {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".sk-global pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip-path: inset(100%);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       ".sk-global div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       ".sk-global label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       ".sk-global div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label label.sk-toggleable__label,\n",
       ".sk-global div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       ".sk-global div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       ".sk-global div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       ".sk-global div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       ".sk-global a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       ".sk-global a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-top-container.sk-global {\n",
       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
       "   to allow keyboard-only users to navigate the display. */\n",
       "  overflow-x: scroll !important;\n",
       "  max-width: 100%;\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover td {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table :is(td, th) {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td, .estimator-table th {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "td.fitted-att-type {\n",
       "    white-space: preserve nowrap;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "@supports(anchor-name: --doc-link) {\n",
       "    a.param-doc-link,\n",
       "    a.param-doc-link:link,\n",
       "    a.param-doc-link:visited {\n",
       "    anchor-name: --doc-link;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "@supports(position-area: center right) {\n",
       "    .param-doc-description {\n",
       "    position-area: center right;\n",
       "    position: fixed;\n",
       "    margin-left: 0;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".features {\n",
       "  font-family: monospace;\n",
       "  cursor: pointer;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: .20em;\n",
       "  margin-bottom: 0.5em;\n",
       "  font-size: inherit; /* Needed for jupyter */\n",
       "}\n",
       "\n",
       ".features.fitted {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features summary {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  margin-bottom: 0;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "  padding: .25em;\n",
       "}\n",
       "\n",
       ".features details[open] > summary {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features.fitted details[open] > summary {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features details > summary .arrow::before {\n",
       "  content: \"▸\";\n",
       "  color: grey;\n",
       "}\n",
       "\n",
       ".features details[open] > summary .arrow::before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       ".features details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".features.fitted details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".features .features-container {\n",
       "  max-width: 15em;\n",
       "  max-height: 10em;\n",
       "  overflow: auto;\n",
       "  scrollbar-width: thin;\n",
       "  padding: .25em 0.1rem;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 0 0 .5em .5em;\n",
       "}\n",
       "\n",
       ".features.fitted .features-container {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features .image-container {\n",
       "  block-size: 1em;\n",
       "  inline-size: 1em;\n",
       "  padding: 0;\n",
       "  margin: 0%;\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  align-items: center;\n",
       "}\n",
       "\n",
       ".features .copy-paste-icon {\n",
       "  background-size: 1em 1em;\n",
       "  width: 1em;\n",
       "  height: 1em;\n",
       "  filter: grayscale(100%) opacity(60%);\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  width: 100%;\n",
       "  margin: 0.01em;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(odd) {\n",
       "  background-color: #fff;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(even) {\n",
       "  background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:hover {\n",
       "  background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  table-layout: inherit;\n",
       "}\n",
       "\n",
       ".features .features-container table td {\n",
       "  text-align: left;\n",
       "  padding: 0 0.5em;\n",
       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "  white-space: nowrap;\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".total_features {\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  margin-top: 0.5em;\n",
       "}\n",
       "</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=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-random_state;\"\n",
       "            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\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-random_state;\">\n",
       "            random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == &#x27;sag&#x27;, &#x27;saga&#x27; or &#x27;liblinear&#x27; to shuffle the<br>data. See :term:`Glossary &lt;random_state&gt;` for details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">42</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-penalty;\"\n",
       "            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\">\n",
       "            penalty\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-penalty;\">\n",
       "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, None}, default=&#x27;l2&#x27;<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `&#x27;l2&#x27;`: add an L2 penalty term and it is the default choice;<br>- `&#x27;l1&#x27;`: add an L1 penalty term;<br>- `&#x27;elasticnet&#x27;`: 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 &#x27;multinomial&#x27; + 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=&#x27;l2&#x27;`,<br>   `l1_ratio=1` for `penalty=&#x27;l1&#x27;`, `l1_ratio` set to any float between 0 and 1<br>   for `penalty=&#x27;elasticnet&#x27;`, and `C=np.inf` for `penalty=None`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('C',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-C;\"\n",
       "            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\">\n",
       "            C\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-C;\">\n",
       "            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>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('l1_ratio',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-l1_ratio;\"\n",
       "            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\">\n",
       "            l1_ratio\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-l1_ratio;\">\n",
       "            l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 &lt;= l1_ratio &lt;= 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>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('dual',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-dual;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
       "            dual\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-dual;\">\n",
       "            dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation &lt;regularized-logistic-loss&gt;`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples &gt; n_features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-tol;\"\n",
       "            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\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-tol;\">\n",
       "            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
       "            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\">\n",
       "            fit_intercept\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
       "            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>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
       "            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\">\n",
       "            intercept_scaling\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
       "            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 &quot;synthetic&quot; 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>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
       "            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\">\n",
       "            class_weight\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
       "            class_weight: dict or &#x27;balanced&#x27;, 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 &quot;balanced&quot; 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=&#x27;balanced&#x27;*</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('solver',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-solver;\"\n",
       "            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\">\n",
       "            solver\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-solver;\">\n",
       "            solver: {&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;newton-cg&#x27;, &#x27;newton-cholesky&#x27;, &#x27;sag&#x27;, &#x27;saga&#x27;},             default=&#x27;lbfgs&#x27;<br><br>Algorithm to use in the optimization problem. Default is &#x27;lbfgs&#x27;.<br>To choose a solver, you might want to consider the following aspects:<br><br>- &#x27;lbfgs&#x27; is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes &gt;= 3`), all solvers except<br>  &#x27;liblinear&#x27; minimize the full multinomial loss, &#x27;liblinear&#x27; will raise an<br>  error.<br>- &#x27;newton-cholesky&#x27; is a good choice for<br>  `n_samples` &gt;&gt; `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, &#x27;liblinear&#x27; is a good choice, whereas &#x27;sag&#x27;<br>  and &#x27;saga&#x27; are faster for large ones;<br>- &#x27;liblinear&#x27; 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 &lt; l1_ratio &lt; 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   &#x27;lbfgs&#x27;           l1_ratio=0               yes<br>   &#x27;liblinear&#x27;       l1_ratio=1 or l1_ratio=0 no<br>   &#x27;newton-cg&#x27;       l1_ratio=0               yes<br>   &#x27;newton-cholesky&#x27; l1_ratio=0               yes<br>   &#x27;sag&#x27;             l1_ratio=0               yes<br>   &#x27;saga&#x27;            0&lt;=l1_ratio&lt;=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   &#x27;sag&#x27; and &#x27;saga&#x27; 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 &lt;Logistic_regression&gt;` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table &lt;logistic_regression_solvers&gt;`<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 &#x27;liblinear&#x27; to &#x27;lbfgs&#x27; in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
       "            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\">\n",
       "            max_iter\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
       "            max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">100</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose;\">\n",
       "            verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-warm_start;\"\n",
       "            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\">\n",
       "            warm_start\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-warm_start;\">\n",
       "            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 &lt;warm_start&gt;`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
       "            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",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
       "            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>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-classes_;\"\n",
       "            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\">\n",
       "            classes_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-classes_;\">\n",
       "            classes_: ndarray of shape (n_classes, )<br><br>A list of class labels known to the classifier.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int64](2,)</td>\n",
       "           <td>[0,1]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-coef_;\"\n",
       "            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\">\n",
       "            coef_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-coef_;\">\n",
       "            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>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](1, 1)</td>\n",
       "           <td>[[-4.53]]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
       "            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\">\n",
       "            intercept_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
       "            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>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
       "           <td>[3.42]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>1</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
       "            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",
       "            n_iter_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
       "            n_iter_: ndarray of shape (1, )<br><br>Actual number of iterations for all classes.<br><br>.. versionchanged:: 0.20<br><br>    In SciPy &lt;= 1.0.0 the number of lbfgs iterations may exceed<br>    ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int32](1,)</td>\n",
       "           <td>[9]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
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      "text/plain": [
       "LogisticRegression(random_state=42)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "mylr = LogisticRegression(solver='lbfgs', random_state=42)\n",
    "mylr.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "dcc26a47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xnew = np.linspace(-1,3,100).reshape(-1,1)\n",
    "yPred = mylr.predict_proba(Xnew)\n",
    "#plt.plot(Xnew, yPred[:,0], label= 'No Iris')\n",
    "plt.plot(Xnew, yPred[:,1], label= 'Yes Iris')\n",
    "plt.legend()\n",
    "plt.plot(x,y,'*g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2732db6f",
   "metadata": {},
   "source": [
    "# Iris-Setosa Classifier based on petal length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4e428962",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = iris.data[:, 2:3]\n",
    "y = (iris.target == 0).astype(int)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "167b1fa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.sk-global {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       ".sk-global.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       ".sk-global.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       ".sk-global {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".sk-global pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip-path: inset(100%);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       ".sk-global div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       ".sk-global label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       ".sk-global div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label label.sk-toggleable__label,\n",
       ".sk-global div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       ".sk-global div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       ".sk-global div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       ".sk-global div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       ".sk-global a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       ".sk-global a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-top-container.sk-global {\n",
       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
       "   to allow keyboard-only users to navigate the display. */\n",
       "  overflow-x: scroll !important;\n",
       "  max-width: 100%;\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover td {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table :is(td, th) {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td, .estimator-table th {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "td.fitted-att-type {\n",
       "    white-space: preserve nowrap;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "@supports(anchor-name: --doc-link) {\n",
       "    a.param-doc-link,\n",
       "    a.param-doc-link:link,\n",
       "    a.param-doc-link:visited {\n",
       "    anchor-name: --doc-link;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "@supports(position-area: center right) {\n",
       "    .param-doc-description {\n",
       "    position-area: center right;\n",
       "    position: fixed;\n",
       "    margin-left: 0;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".features {\n",
       "  font-family: monospace;\n",
       "  cursor: pointer;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: .20em;\n",
       "  margin-bottom: 0.5em;\n",
       "  font-size: inherit; /* Needed for jupyter */\n",
       "}\n",
       "\n",
       ".features.fitted {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features summary {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  margin-bottom: 0;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "  padding: .25em;\n",
       "}\n",
       "\n",
       ".features details[open] > summary {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features.fitted details[open] > summary {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features details > summary .arrow::before {\n",
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       "  color: grey;\n",
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       "\n",
       ".features details[open] > summary .arrow::before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       ".features details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".features.fitted details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".features .features-container {\n",
       "  max-width: 15em;\n",
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       "  border-radius: 0 0 .5em .5em;\n",
       "}\n",
       "\n",
       ".features.fitted .features-container {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features .image-container {\n",
       "  block-size: 1em;\n",
       "  inline-size: 1em;\n",
       "  padding: 0;\n",
       "  margin: 0%;\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  align-items: center;\n",
       "}\n",
       "\n",
       ".features .copy-paste-icon {\n",
       "  background-size: 1em 1em;\n",
       "  width: 1em;\n",
       "  height: 1em;\n",
       "  filter: grayscale(100%) opacity(60%);\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  width: 100%;\n",
       "  margin: 0.01em;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(odd) {\n",
       "  background-color: #fff;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(even) {\n",
       "  background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:hover {\n",
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       "\n",
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       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "  white-space: nowrap;\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".total_features {\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  margin-top: 0.5em;\n",
       "}\n",
       "</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=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-random_state;\"\n",
       "            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\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-random_state;\">\n",
       "            random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == &#x27;sag&#x27;, &#x27;saga&#x27; or &#x27;liblinear&#x27; to shuffle the<br>data. See :term:`Glossary &lt;random_state&gt;` for details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">42</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-penalty;\"\n",
       "            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\">\n",
       "            penalty\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-penalty;\">\n",
       "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, None}, default=&#x27;l2&#x27;<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `&#x27;l2&#x27;`: add an L2 penalty term and it is the default choice;<br>- `&#x27;l1&#x27;`: add an L1 penalty term;<br>- `&#x27;elasticnet&#x27;`: 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 &#x27;multinomial&#x27; + 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=&#x27;l2&#x27;`,<br>   `l1_ratio=1` for `penalty=&#x27;l1&#x27;`, `l1_ratio` set to any float between 0 and 1<br>   for `penalty=&#x27;elasticnet&#x27;`, and `C=np.inf` for `penalty=None`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('C',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-C;\"\n",
       "            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\">\n",
       "            C\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-C;\">\n",
       "            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>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('l1_ratio',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-l1_ratio;\"\n",
       "            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\">\n",
       "            l1_ratio\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-l1_ratio;\">\n",
       "            l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 &lt;= l1_ratio &lt;= 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>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('dual',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-dual;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
       "            dual\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-dual;\">\n",
       "            dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation &lt;regularized-logistic-loss&gt;`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples &gt; n_features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-tol;\"\n",
       "            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\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-tol;\">\n",
       "            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
       "            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\">\n",
       "            fit_intercept\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
       "            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>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
       "            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\">\n",
       "            intercept_scaling\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
       "            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 &quot;synthetic&quot; 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>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
       "            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\">\n",
       "            class_weight\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
       "            class_weight: dict or &#x27;balanced&#x27;, 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 &quot;balanced&quot; 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=&#x27;balanced&#x27;*</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('solver',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-solver;\"\n",
       "            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\">\n",
       "            solver\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-solver;\">\n",
       "            solver: {&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;newton-cg&#x27;, &#x27;newton-cholesky&#x27;, &#x27;sag&#x27;, &#x27;saga&#x27;},             default=&#x27;lbfgs&#x27;<br><br>Algorithm to use in the optimization problem. Default is &#x27;lbfgs&#x27;.<br>To choose a solver, you might want to consider the following aspects:<br><br>- &#x27;lbfgs&#x27; is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes &gt;= 3`), all solvers except<br>  &#x27;liblinear&#x27; minimize the full multinomial loss, &#x27;liblinear&#x27; will raise an<br>  error.<br>- &#x27;newton-cholesky&#x27; is a good choice for<br>  `n_samples` &gt;&gt; `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, &#x27;liblinear&#x27; is a good choice, whereas &#x27;sag&#x27;<br>  and &#x27;saga&#x27; are faster for large ones;<br>- &#x27;liblinear&#x27; 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 &lt; l1_ratio &lt; 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   &#x27;lbfgs&#x27;           l1_ratio=0               yes<br>   &#x27;liblinear&#x27;       l1_ratio=1 or l1_ratio=0 no<br>   &#x27;newton-cg&#x27;       l1_ratio=0               yes<br>   &#x27;newton-cholesky&#x27; l1_ratio=0               yes<br>   &#x27;sag&#x27;             l1_ratio=0               yes<br>   &#x27;saga&#x27;            0&lt;=l1_ratio&lt;=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   &#x27;sag&#x27; and &#x27;saga&#x27; 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 &lt;Logistic_regression&gt;` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table &lt;logistic_regression_solvers&gt;`<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 &#x27;liblinear&#x27; to &#x27;lbfgs&#x27; in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
       "            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\">\n",
       "            max_iter\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
       "            max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">100</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose;\">\n",
       "            verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-warm_start;\"\n",
       "            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\">\n",
       "            warm_start\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-warm_start;\">\n",
       "            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 &lt;warm_start&gt;`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
       "            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",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
       "            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>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-classes_;\"\n",
       "            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\">\n",
       "            classes_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-classes_;\">\n",
       "            classes_: ndarray of shape (n_classes, )<br><br>A list of class labels known to the classifier.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int64](2,)</td>\n",
       "           <td>[0,1]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-coef_;\"\n",
       "            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\">\n",
       "            coef_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-coef_;\">\n",
       "            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>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](1, 1)</td>\n",
       "           <td>[[-2.92]]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
       "            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\">\n",
       "            intercept_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
       "            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>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
       "           <td>[7.92]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>1</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
       "            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",
       "            n_iter_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
       "            n_iter_: ndarray of shape (1, )<br><br>Actual number of iterations for all classes.<br><br>.. versionchanged:: 0.20<br><br>    In SciPy &lt;= 1.0.0 the number of lbfgs iterations may exceed<br>    ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int32](1,)</td>\n",
       "           <td>[17]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
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      "text/plain": [
       "LogisticRegression(random_state=42)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "mylr = LogisticRegression(solver='lbfgs', random_state=42)\n",
    "mylr.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1be05124",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xnew = np.linspace(0,8,100).reshape(-1,1)\n",
    "yPred = mylr.predict_proba(Xnew)\n",
    "#plt.plot(Xnew, yPred[:,0], label= 'No Iris')\n",
    "plt.plot(Xnew, yPred[:,1], label= 'Yes Iris')\n",
    "plt.legend()\n",
    "plt.plot(x,y,'*g')\n",
    "plt.axis([1.5, 5, -0.5, 1.5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87ce64b0",
   "metadata": {},
   "source": [
    "# Iris-Setosa Classifier based on Sepal length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "78ea8db6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.sk-global {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       ".sk-global.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       ".sk-global.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       ".sk-global {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".sk-global pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip-path: inset(100%);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       ".sk-global div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       ".sk-global label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       ".sk-global div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label label.sk-toggleable__label,\n",
       ".sk-global div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       ".sk-global div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       ".sk-global div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       ".sk-global div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       ".sk-global a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       ".sk-global a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-top-container.sk-global {\n",
       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
       "   to allow keyboard-only users to navigate the display. */\n",
       "  overflow-x: scroll !important;\n",
       "  max-width: 100%;\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover td {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table :is(td, th) {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td, .estimator-table th {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "td.fitted-att-type {\n",
       "    white-space: preserve nowrap;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "@supports(anchor-name: --doc-link) {\n",
       "    a.param-doc-link,\n",
       "    a.param-doc-link:link,\n",
       "    a.param-doc-link:visited {\n",
       "    anchor-name: --doc-link;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "@supports(position-area: center right) {\n",
       "    .param-doc-description {\n",
       "    position-area: center right;\n",
       "    position: fixed;\n",
       "    margin-left: 0;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
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       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".features {\n",
       "  font-family: monospace;\n",
       "  cursor: pointer;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: .20em;\n",
       "  margin-bottom: 0.5em;\n",
       "  font-size: inherit; /* Needed for jupyter */\n",
       "}\n",
       "\n",
       ".features.fitted {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features summary {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  margin-bottom: 0;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "  padding: .25em;\n",
       "}\n",
       "\n",
       ".features details[open] > summary {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features.fitted details[open] > summary {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features details > summary .arrow::before {\n",
       "  content: \"▸\";\n",
       "  color: grey;\n",
       "}\n",
       "\n",
       ".features details[open] > summary .arrow::before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       ".features details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".features.fitted details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".features .features-container {\n",
       "  max-width: 15em;\n",
       "  max-height: 10em;\n",
       "  overflow: auto;\n",
       "  scrollbar-width: thin;\n",
       "  padding: .25em 0.1rem;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 0 0 .5em .5em;\n",
       "}\n",
       "\n",
       ".features.fitted .features-container {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features .image-container {\n",
       "  block-size: 1em;\n",
       "  inline-size: 1em;\n",
       "  padding: 0;\n",
       "  margin: 0%;\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  align-items: center;\n",
       "}\n",
       "\n",
       ".features .copy-paste-icon {\n",
       "  background-size: 1em 1em;\n",
       "  width: 1em;\n",
       "  height: 1em;\n",
       "  filter: grayscale(100%) opacity(60%);\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  width: 100%;\n",
       "  margin: 0.01em;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(odd) {\n",
       "  background-color: #fff;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(even) {\n",
       "  background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:hover {\n",
       "  background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  table-layout: inherit;\n",
       "}\n",
       "\n",
       ".features .features-container table td {\n",
       "  text-align: left;\n",
       "  padding: 0 0.5em;\n",
       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "  white-space: nowrap;\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".total_features {\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  margin-top: 0.5em;\n",
       "}\n",
       "</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=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-random_state;\"\n",
       "            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\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-random_state;\">\n",
       "            random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == &#x27;sag&#x27;, &#x27;saga&#x27; or &#x27;liblinear&#x27; to shuffle the<br>data. See :term:`Glossary &lt;random_state&gt;` for details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">42</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-penalty;\"\n",
       "            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\">\n",
       "            penalty\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-penalty;\">\n",
       "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, None}, default=&#x27;l2&#x27;<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `&#x27;l2&#x27;`: add an L2 penalty term and it is the default choice;<br>- `&#x27;l1&#x27;`: add an L1 penalty term;<br>- `&#x27;elasticnet&#x27;`: 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 &#x27;multinomial&#x27; + 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=&#x27;l2&#x27;`,<br>   `l1_ratio=1` for `penalty=&#x27;l1&#x27;`, `l1_ratio` set to any float between 0 and 1<br>   for `penalty=&#x27;elasticnet&#x27;`, and `C=np.inf` for `penalty=None`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('C',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-C;\"\n",
       "            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\">\n",
       "            C\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-C;\">\n",
       "            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>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('l1_ratio',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-l1_ratio;\"\n",
       "            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\">\n",
       "            l1_ratio\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-l1_ratio;\">\n",
       "            l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 &lt;= l1_ratio &lt;= 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>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('dual',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-dual;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
       "            dual\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-dual;\">\n",
       "            dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation &lt;regularized-logistic-loss&gt;`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples &gt; n_features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-tol;\"\n",
       "            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\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-tol;\">\n",
       "            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
       "            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\">\n",
       "            fit_intercept\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
       "            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>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
       "            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\">\n",
       "            intercept_scaling\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
       "            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 &quot;synthetic&quot; 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>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
       "            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\">\n",
       "            class_weight\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
       "            class_weight: dict or &#x27;balanced&#x27;, 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 &quot;balanced&quot; 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=&#x27;balanced&#x27;*</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('solver',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-solver;\"\n",
       "            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\">\n",
       "            solver\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-solver;\">\n",
       "            solver: {&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;newton-cg&#x27;, &#x27;newton-cholesky&#x27;, &#x27;sag&#x27;, &#x27;saga&#x27;},             default=&#x27;lbfgs&#x27;<br><br>Algorithm to use in the optimization problem. Default is &#x27;lbfgs&#x27;.<br>To choose a solver, you might want to consider the following aspects:<br><br>- &#x27;lbfgs&#x27; is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes &gt;= 3`), all solvers except<br>  &#x27;liblinear&#x27; minimize the full multinomial loss, &#x27;liblinear&#x27; will raise an<br>  error.<br>- &#x27;newton-cholesky&#x27; is a good choice for<br>  `n_samples` &gt;&gt; `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, &#x27;liblinear&#x27; is a good choice, whereas &#x27;sag&#x27;<br>  and &#x27;saga&#x27; are faster for large ones;<br>- &#x27;liblinear&#x27; 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 &lt; l1_ratio &lt; 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   &#x27;lbfgs&#x27;           l1_ratio=0               yes<br>   &#x27;liblinear&#x27;       l1_ratio=1 or l1_ratio=0 no<br>   &#x27;newton-cg&#x27;       l1_ratio=0               yes<br>   &#x27;newton-cholesky&#x27; l1_ratio=0               yes<br>   &#x27;sag&#x27;             l1_ratio=0               yes<br>   &#x27;saga&#x27;            0&lt;=l1_ratio&lt;=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   &#x27;sag&#x27; and &#x27;saga&#x27; 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 &lt;Logistic_regression&gt;` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table &lt;logistic_regression_solvers&gt;`<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 &#x27;liblinear&#x27; to &#x27;lbfgs&#x27; in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
       "            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\">\n",
       "            max_iter\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
       "            max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">100</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose;\">\n",
       "            verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-warm_start;\"\n",
       "            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\">\n",
       "            warm_start\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-warm_start;\">\n",
       "            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 &lt;warm_start&gt;`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
       "            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",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
       "            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>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-classes_;\"\n",
       "            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\">\n",
       "            classes_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-classes_;\">\n",
       "            classes_: ndarray of shape (n_classes, )<br><br>A list of class labels known to the classifier.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int64](2,)</td>\n",
       "           <td>[0,1]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-coef_;\"\n",
       "            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\">\n",
       "            coef_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-coef_;\">\n",
       "            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>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](1, 1)</td>\n",
       "           <td>[[-3.46]]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
       "            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\">\n",
       "            intercept_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
       "            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>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
       "           <td>[18.55]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>1</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
       "            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",
       "            n_iter_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
       "            n_iter_: ndarray of shape (1, )<br><br>Actual number of iterations for all classes.<br><br>.. versionchanged:: 0.20<br><br>    In SciPy &lt;= 1.0.0 the number of lbfgs iterations may exceed<br>    ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int32](1,)</td>\n",
       "           <td>[17]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
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      "text/plain": [
       "LogisticRegression(random_state=42)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = iris.data[:, 0:1]\n",
    "y = (iris.target == 0).astype(int)\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "mylr = LogisticRegression(solver='lbfgs', random_state=42)\n",
    "mylr.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ff8a3c01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xnew = np.linspace(0,8,100).reshape(-1,1)\n",
    "yPred = mylr.predict_proba(Xnew)\n",
    "#plt.plot(Xnew, yPred[:,0], label= 'No Iris')\n",
    "plt.plot(Xnew, yPred[:,1], label= 'Yes Iris')\n",
    "plt.legend()\n",
    "plt.plot(x,y,'*g')\n",
    "plt.axis([3.5, 7, -0.1, 1.1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "344cf161",
   "metadata": {},
   "source": [
    "# Multiple features classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "c2bae4e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "sl = iris.data[:,0:1]\n",
    "sw = iris.data[:,1:2]\n",
    "tg = iris.target\n",
    "plt.plot(sl[tg==0,0], sw[tg==0,0],'.g' ,label='Set')\n",
    "plt.plot(sl[tg==1,0], sw[tg==1,0],'.r', label='Ver')\n",
    "plt.plot(sl[tg==2,0], sw[tg==2,0],'.b', label='Vir')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ce1caddc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.sk-global {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       ".sk-global.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       ".sk-global.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       ".sk-global {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".sk-global pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip-path: inset(100%);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       ".sk-global div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       ".sk-global label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       ".sk-global div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label label.sk-toggleable__label,\n",
       ".sk-global div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       ".sk-global div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       ".sk-global div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       ".sk-global div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       ".sk-global a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       ".sk-global a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-top-container.sk-global {\n",
       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
       "   to allow keyboard-only users to navigate the display. */\n",
       "  overflow-x: scroll !important;\n",
       "  max-width: 100%;\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover td {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table :is(td, th) {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td, .estimator-table th {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "td.fitted-att-type {\n",
       "    white-space: preserve nowrap;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "@supports(anchor-name: --doc-link) {\n",
       "    a.param-doc-link,\n",
       "    a.param-doc-link:link,\n",
       "    a.param-doc-link:visited {\n",
       "    anchor-name: --doc-link;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "@supports(position-area: center right) {\n",
       "    .param-doc-description {\n",
       "    position-area: center right;\n",
       "    position: fixed;\n",
       "    margin-left: 0;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".features {\n",
       "  font-family: monospace;\n",
       "  cursor: pointer;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: .20em;\n",
       "  margin-bottom: 0.5em;\n",
       "  font-size: inherit; /* Needed for jupyter */\n",
       "}\n",
       "\n",
       ".features.fitted {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features summary {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  margin-bottom: 0;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "  padding: .25em;\n",
       "}\n",
       "\n",
       ".features details[open] > summary {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features.fitted details[open] > summary {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features details > summary .arrow::before {\n",
       "  content: \"▸\";\n",
       "  color: grey;\n",
       "}\n",
       "\n",
       ".features details[open] > summary .arrow::before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       ".features details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".features.fitted details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".features .features-container {\n",
       "  max-width: 15em;\n",
       "  max-height: 10em;\n",
       "  overflow: auto;\n",
       "  scrollbar-width: thin;\n",
       "  padding: .25em 0.1rem;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 0 0 .5em .5em;\n",
       "}\n",
       "\n",
       ".features.fitted .features-container {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features .image-container {\n",
       "  block-size: 1em;\n",
       "  inline-size: 1em;\n",
       "  padding: 0;\n",
       "  margin: 0%;\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  align-items: center;\n",
       "}\n",
       "\n",
       ".features .copy-paste-icon {\n",
       "  background-size: 1em 1em;\n",
       "  width: 1em;\n",
       "  height: 1em;\n",
       "  filter: grayscale(100%) opacity(60%);\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  width: 100%;\n",
       "  margin: 0.01em;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(odd) {\n",
       "  background-color: #fff;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(even) {\n",
       "  background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:hover {\n",
       "  background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  table-layout: inherit;\n",
       "}\n",
       "\n",
       ".features .features-container table td {\n",
       "  text-align: left;\n",
       "  padding: 0 0.5em;\n",
       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "  white-space: nowrap;\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".total_features {\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  margin-top: 0.5em;\n",
       "}\n",
       "</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=&#x27;newton-cg&#x27;, 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=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('C',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-C;\"\n",
       "            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\">\n",
       "            C\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-C;\">\n",
       "            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>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">100</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-tol;\"\n",
       "            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\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-tol;\">\n",
       "            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1e-05</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-random_state;\"\n",
       "            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\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-random_state;\">\n",
       "            random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == &#x27;sag&#x27;, &#x27;saga&#x27; or &#x27;liblinear&#x27; to shuffle the<br>data. See :term:`Glossary &lt;random_state&gt;` for details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">22</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('solver',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-solver;\"\n",
       "            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\">\n",
       "            solver\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-solver;\">\n",
       "            solver: {&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;newton-cg&#x27;, &#x27;newton-cholesky&#x27;, &#x27;sag&#x27;, &#x27;saga&#x27;},             default=&#x27;lbfgs&#x27;<br><br>Algorithm to use in the optimization problem. Default is &#x27;lbfgs&#x27;.<br>To choose a solver, you might want to consider the following aspects:<br><br>- &#x27;lbfgs&#x27; is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes &gt;= 3`), all solvers except<br>  &#x27;liblinear&#x27; minimize the full multinomial loss, &#x27;liblinear&#x27; will raise an<br>  error.<br>- &#x27;newton-cholesky&#x27; is a good choice for<br>  `n_samples` &gt;&gt; `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, &#x27;liblinear&#x27; is a good choice, whereas &#x27;sag&#x27;<br>  and &#x27;saga&#x27; are faster for large ones;<br>- &#x27;liblinear&#x27; 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 &lt; l1_ratio &lt; 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   &#x27;lbfgs&#x27;           l1_ratio=0               yes<br>   &#x27;liblinear&#x27;       l1_ratio=1 or l1_ratio=0 no<br>   &#x27;newton-cg&#x27;       l1_ratio=0               yes<br>   &#x27;newton-cholesky&#x27; l1_ratio=0               yes<br>   &#x27;sag&#x27;             l1_ratio=0               yes<br>   &#x27;saga&#x27;            0&lt;=l1_ratio&lt;=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   &#x27;sag&#x27; and &#x27;saga&#x27; 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 &lt;Logistic_regression&gt;` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table &lt;logistic_regression_solvers&gt;`<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 &#x27;liblinear&#x27; to &#x27;lbfgs&#x27; in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;newton-cg&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-penalty;\"\n",
       "            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\">\n",
       "            penalty\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-penalty;\">\n",
       "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, None}, default=&#x27;l2&#x27;<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `&#x27;l2&#x27;`: add an L2 penalty term and it is the default choice;<br>- `&#x27;l1&#x27;`: add an L1 penalty term;<br>- `&#x27;elasticnet&#x27;`: 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 &#x27;multinomial&#x27; + 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=&#x27;l2&#x27;`,<br>   `l1_ratio=1` for `penalty=&#x27;l1&#x27;`, `l1_ratio` set to any float between 0 and 1<br>   for `penalty=&#x27;elasticnet&#x27;`, and `C=np.inf` for `penalty=None`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('l1_ratio',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-l1_ratio;\"\n",
       "            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\">\n",
       "            l1_ratio\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-l1_ratio;\">\n",
       "            l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 &lt;= l1_ratio &lt;= 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>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('dual',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-dual;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
       "            dual\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-dual;\">\n",
       "            dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation &lt;regularized-logistic-loss&gt;`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples &gt; n_features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
       "            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\">\n",
       "            fit_intercept\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
       "            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>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
       "            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\">\n",
       "            intercept_scaling\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
       "            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 &quot;synthetic&quot; 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>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
       "            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\">\n",
       "            class_weight\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
       "            class_weight: dict or &#x27;balanced&#x27;, 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 &quot;balanced&quot; 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=&#x27;balanced&#x27;*</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
       "            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\">\n",
       "            max_iter\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
       "            max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">100</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose;\">\n",
       "            verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-warm_start;\"\n",
       "            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\">\n",
       "            warm_start\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-warm_start;\">\n",
       "            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 &lt;warm_start&gt;`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
       "            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",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
       "            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>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-classes_;\"\n",
       "            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\">\n",
       "            classes_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-classes_;\">\n",
       "            classes_: ndarray of shape (n_classes, )<br><br>A list of class labels known to the classifier.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int64](2,)</td>\n",
       "           <td>[0,1]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-coef_;\"\n",
       "            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\">\n",
       "            coef_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-coef_;\">\n",
       "            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>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](1, 2)</td>\n",
       "           <td>[[ 2.6 ,-0.74]]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
       "            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\">\n",
       "            intercept_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
       "            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>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](1,)</td>\n",
       "           <td>[-14.16]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>2</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
       "            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",
       "            n_iter_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
       "            n_iter_: ndarray of shape (1, )<br><br>Actual number of iterations for all classes.<br><br>.. versionchanged:: 0.20<br><br>    In SciPy &lt;= 1.0.0 the number of lbfgs iterations may exceed<br>    ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int32](1,)</td>\n",
       "           <td>[11]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
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      "text/plain": [
       "LogisticRegression(C=100, random_state=22, solver='newton-cg', tol=1e-05)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = iris.data[:,0:2]\n",
    "y = (iris.target==2).astype(int)\n",
    "mylrvir = LogisticRegression(\n",
    "    random_state=22,\n",
    "    tol=1e-5,\n",
    "    C=100,\n",
    "    max_iter=100,\n",
    "    solver='newton-cg'\n",
    ")\n",
    "mylrvir.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "9db6e30b",
   "metadata": {},
   "outputs": [],
   "source": [
    "x0, x1 = np.meshgrid(\n",
    "    np.linspace(3,8,100).reshape(-1,1),\n",
    "    np.linspace(0,6,100).reshape(-1,1)\n",
    ")\n",
    "Xnew = np.c_[x0.ravel(), x1.ravel()]\n",
    "yPred = mylrvir.predict_proba(Xnew)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "b7684723",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "plt.plot(X[y==0,0], X[y==0,1],'bs',label='No Virg')\n",
    "plt.plot(X[y==1,0], X[y==1,1],'g^',label='Virginica')\n",
    "zz=yPred[:,1].reshape(x0.shape)\n",
    "contour=plt.contour(x0,x1,zz)\n",
    "plt.clabel(contour, inline=1,fontsize=15)\n",
    "plt.xlabel(\"Sepal Length\")\n",
    "plt.ylabel(\"Sepal Width\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "0b4ce6e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x16c2b47ee40>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax =plt.subplots(subplot_kw={\"projection\": \"3d\"})\n",
    "surf = ax.plot_surface(x0,x1,zz, cmap='jet')\n",
    "ax.scatter(iris.data[:,0:1], iris.data[:,1:2], y, 'or')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "441defe5",
   "metadata": {},
   "source": [
    "# Multiple features and muticlass classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "5112f48b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.sk-global {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: #000;\n",
       "  --sklearn-color-text-muted: #666;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "}\n",
       "\n",
       ".sk-global.light {\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: black;\n",
       "  --sklearn-color-background: white;\n",
       "  --sklearn-color-border-box: black;\n",
       "  --sklearn-color-icon: #696969;\n",
       "}\n",
       "\n",
       ".sk-global.dark {\n",
       "  --sklearn-color-text-on-default-background: white;\n",
       "  --sklearn-color-background: #111;\n",
       "  --sklearn-color-border-box: white;\n",
       "  --sklearn-color-icon: #878787;\n",
       "}\n",
       "\n",
       ".sk-global {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".sk-global pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip-path: inset(100%);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       ".sk-global div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       ".sk-global div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       ".sk-global label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label .caption {\n",
       "  font-size: 0.6rem;\n",
       "  font-weight: lighter;\n",
       "  color: var(--sklearn-color-text-muted);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       ".sk-global label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       ".sk-global div.sk-toggleable__content {\n",
       "  display: none;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  overflow: visible;\n",
       "}\n",
       "\n",
       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       ".sk-global div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       ".sk-global div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label label.sk-toggleable__label,\n",
       ".sk-global div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       ".sk-global div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       ".sk-global div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       ".sk-global div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       ".sk-global div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       ".sk-global div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       ".sk-global div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".sk-global div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 0.5em;\n",
       "  text-align: center;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-0);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       ".sk-global a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       ".sk-global a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       ".sk-global a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-top-container.sk-global {\n",
       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
       "   to allow keyboard-only users to navigate the display. */\n",
       "  overflow-x: scroll !important;\n",
       "  max-width: 100%;\n",
       "}\n",
       "\n",
       ".estimator-table {\n",
       "    font-family: monospace;\n",
       "}\n",
       "\n",
       ".estimator-table summary {\n",
       "    padding: .5rem;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".estimator-table summary::marker {\n",
       "    font-size: 0.7rem;\n",
       "}\n",
       "\n",
       ".estimator-table details[open] {\n",
       "    padding-left: 0.1rem;\n",
       "    padding-right: 0.1rem;\n",
       "    padding-bottom: 0.3rem;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table {\n",
       "    margin-left: auto !important;\n",
       "    margin-right: auto !important;\n",
       "    margin-top: 0;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
       "    background-color: #fff;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:nth-child(even) {\n",
       "    background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".estimator-table .parameters-table tr:hover td {\n",
       "    background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".estimator-table table :is(td, th) {\n",
       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "}\n",
       "\n",
       "/*\n",
       "    `table td`is set in notebook with right text-align.\n",
       "    We need to overwrite it.\n",
       "*/\n",
       ".estimator-table table td.param {\n",
       "    text-align: left;\n",
       "    position: relative;\n",
       "    padding: 0;\n",
       "}\n",
       "\n",
       ".user-set td {\n",
       "    color:rgb(255, 94, 0);\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td.value {\n",
       "    color:rgb(255, 94, 0);\n",
       "    background-color: transparent;\n",
       "}\n",
       "\n",
       ".default td, .estimator-table th {\n",
       "    color: black;\n",
       "    text-align: left !important;\n",
       "}\n",
       "\n",
       ".user-set td i,\n",
       ".default td i {\n",
       "    color: black;\n",
       "}\n",
       "\n",
       "td.fitted-att-type {\n",
       "    white-space: preserve nowrap;\n",
       "}\n",
       "\n",
       "/*\n",
       "    Styles for parameter documentation links\n",
       "    We need styling for visited so jupyter doesn't overwrite it\n",
       "*/\n",
       "a.param-doc-link,\n",
       "a.param-doc-link:link,\n",
       "a.param-doc-link:visited {\n",
       "    text-decoration: underline dashed;\n",
       "    text-underline-offset: .3em;\n",
       "    color: inherit;\n",
       "    display: block;\n",
       "    padding: .5em;\n",
       "}\n",
       "\n",
       "@supports(anchor-name: --doc-link) {\n",
       "    a.param-doc-link,\n",
       "    a.param-doc-link:link,\n",
       "    a.param-doc-link:visited {\n",
       "    anchor-name: --doc-link;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
       "a.param-doc-link::before {\n",
       "    position: absolute;\n",
       "    content: \"\";\n",
       "    inset: 0;\n",
       "}\n",
       "\n",
       ".param-doc-description {\n",
       "    display: none;\n",
       "    position: absolute;\n",
       "    z-index: 9999;\n",
       "    left: 0;\n",
       "    padding: .5ex;\n",
       "    margin-left: 1.5em;\n",
       "    color: var(--sklearn-color-text);\n",
       "    box-shadow: .3em .3em .4em #999;\n",
       "    width: max-content;\n",
       "    text-align: left;\n",
       "    max-height: 10em;\n",
       "    overflow-y: auto;\n",
       "\n",
       "    /* unfitted */\n",
       "    background: var(--sklearn-color-unfitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       "@supports(position-area: center right) {\n",
       "    .param-doc-description {\n",
       "    position-area: center right;\n",
       "    position: fixed;\n",
       "    margin-left: 0;\n",
       "    }\n",
       "}\n",
       "\n",
       "/* Fitted state for parameter tooltips */\n",
       ".fitted .param-doc-description {\n",
       "    /* fitted */\n",
       "    background: var(--sklearn-color-fitted-level-0);\n",
       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".param-doc-link:hover .param-doc-description {\n",
       "    display: block;\n",
       "}\n",
       "\n",
       ".copy-paste-icon {\n",
       "    background-image: url(data:image/svg+xml;base64,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);\n",
       "    background-repeat: no-repeat;\n",
       "    background-size: 14px 14px;\n",
       "    background-position: 0;\n",
       "    display: inline-block;\n",
       "    width: 14px;\n",
       "    height: 14px;\n",
       "    cursor: pointer;\n",
       "}\n",
       "\n",
       ".features {\n",
       "  font-family: monospace;\n",
       "  cursor: pointer;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: .20em;\n",
       "  margin-bottom: 0.5em;\n",
       "  font-size: inherit; /* Needed for jupyter */\n",
       "}\n",
       "\n",
       ".features.fitted {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features summary {\n",
       "  cursor: pointer;\n",
       "  display: flex;\n",
       "  margin-bottom: 0;\n",
       "  text-align: center;\n",
       "  align-items: center;\n",
       "  justify-content: center;\n",
       "  gap: 0.5em;\n",
       "  padding: .25em;\n",
       "}\n",
       "\n",
       ".features details[open] > summary {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features.fitted details[open] > summary {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "  border-radius: .20em 0 0 0;\n",
       "}\n",
       "\n",
       ".features details > summary .arrow::before {\n",
       "  content: \"▸\";\n",
       "  color: grey;\n",
       "}\n",
       "\n",
       ".features details[open] > summary .arrow::before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       ".features details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       ".features.fitted details:hover > summary {\n",
       "  margin: 0;\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       ".features .features-container {\n",
       "  max-width: 15em;\n",
       "  max-height: 10em;\n",
       "  overflow: auto;\n",
       "  scrollbar-width: thin;\n",
       "  padding: .25em 0.1rem;\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "  border-radius: 0 0 .5em .5em;\n",
       "}\n",
       "\n",
       ".features.fitted .features-container {\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       ".features .image-container {\n",
       "  block-size: 1em;\n",
       "  inline-size: 1em;\n",
       "  padding: 0;\n",
       "  margin: 0%;\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  align-items: center;\n",
       "}\n",
       "\n",
       ".features .copy-paste-icon {\n",
       "  background-size: 1em 1em;\n",
       "  width: 1em;\n",
       "  height: 1em;\n",
       "  filter: grayscale(100%) opacity(60%);\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  width: 100%;\n",
       "  margin: 0.01em;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(odd) {\n",
       "  background-color: #fff;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:nth-child(even) {\n",
       "  background-color: #f6f6f6;\n",
       "}\n",
       "\n",
       ".features .features-container table tr:hover {\n",
       "  background-color: #e0e0e0;\n",
       "}\n",
       "\n",
       ".features .features-container table {\n",
       "  table-layout: inherit;\n",
       "}\n",
       "\n",
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       "  text-align: left;\n",
       "  padding: 0 0.5em;\n",
       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
       "  white-space: nowrap;\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       ".total_features {\n",
       "  display: flex;\n",
       "  justify-content: center;\n",
       "  margin-top: 0.5em;\n",
       "}\n",
       "</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=\"\">\n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Parameters</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                  <tbody>\n",
       "                    \n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('C',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-C;\"\n",
       "            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\">\n",
       "            C\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-C;\">\n",
       "            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>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">100</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"user-set\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('random_state',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-random_state;\"\n",
       "            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\">\n",
       "            random_state\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-random_state;\">\n",
       "            random_state: int, RandomState instance, default=None<br><br>Used when ``solver`` == &#x27;sag&#x27;, &#x27;saga&#x27; or &#x27;liblinear&#x27; to shuffle the<br>data. See :term:`Glossary &lt;random_state&gt;` for details.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">22</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('penalty',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-penalty;\"\n",
       "            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\">\n",
       "            penalty\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-penalty;\">\n",
       "            penalty: {&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;, None}, default=&#x27;l2&#x27;<br><br>Specify the norm of the penalty:<br><br>- `None`: no penalty is added;<br>- `&#x27;l2&#x27;`: add an L2 penalty term and it is the default choice;<br>- `&#x27;l1&#x27;`: add an L1 penalty term;<br>- `&#x27;elasticnet&#x27;`: 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 &#x27;multinomial&#x27; + 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=&#x27;l2&#x27;`,<br>   `l1_ratio=1` for `penalty=&#x27;l1&#x27;`, `l1_ratio` set to any float between 0 and 1<br>   for `penalty=&#x27;elasticnet&#x27;`, and `C=np.inf` for `penalty=None`.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;deprecated&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('l1_ratio',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-l1_ratio;\"\n",
       "            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\">\n",
       "            l1_ratio\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-l1_ratio;\">\n",
       "            l1_ratio: float, default=0.0<br><br>The Elastic-Net mixing parameter, with `0 &lt;= l1_ratio &lt;= 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>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('dual',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-dual;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=dual,-bool%2C%20default%3DFalse\">\n",
       "            dual\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-dual;\">\n",
       "            dual: bool, default=False<br><br>Dual (constrained) or primal (regularized, see also<br>:ref:`this equation &lt;regularized-logistic-loss&gt;`) formulation. Dual formulation<br>is only implemented for l2 penalty with liblinear solver. Prefer `dual=False`<br>when n_samples &gt; n_features.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('tol',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-tol;\"\n",
       "            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\">\n",
       "            tol\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-tol;\">\n",
       "            tol: float, default=1e-4<br><br>Tolerance for stopping criteria.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0.0001</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('fit_intercept',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-fit_intercept;\"\n",
       "            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\">\n",
       "            fit_intercept\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-fit_intercept;\">\n",
       "            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>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">True</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('intercept_scaling',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-intercept_scaling;\"\n",
       "            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\">\n",
       "            intercept_scaling\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_scaling;\">\n",
       "            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 &quot;synthetic&quot; 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>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">1</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('class_weight',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-class_weight;\"\n",
       "            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\">\n",
       "            class_weight\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-class_weight;\">\n",
       "            class_weight: dict or &#x27;balanced&#x27;, 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 &quot;balanced&quot; 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=&#x27;balanced&#x27;*</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('solver',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-solver;\"\n",
       "            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\">\n",
       "            solver\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-solver;\">\n",
       "            solver: {&#x27;lbfgs&#x27;, &#x27;liblinear&#x27;, &#x27;newton-cg&#x27;, &#x27;newton-cholesky&#x27;, &#x27;sag&#x27;, &#x27;saga&#x27;},             default=&#x27;lbfgs&#x27;<br><br>Algorithm to use in the optimization problem. Default is &#x27;lbfgs&#x27;.<br>To choose a solver, you might want to consider the following aspects:<br><br>- &#x27;lbfgs&#x27; is a good default solver because it works reasonably well for a wide<br>  class of problems.<br>- For :term:`multiclass` problems (`n_classes &gt;= 3`), all solvers except<br>  &#x27;liblinear&#x27; minimize the full multinomial loss, &#x27;liblinear&#x27; will raise an<br>  error.<br>- &#x27;newton-cholesky&#x27; is a good choice for<br>  `n_samples` &gt;&gt; `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, &#x27;liblinear&#x27; is a good choice, whereas &#x27;sag&#x27;<br>  and &#x27;saga&#x27; are faster for large ones;<br>- &#x27;liblinear&#x27; 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 &lt; l1_ratio &lt; 1` for<br>   Elastic-Net) and on (multinomial) multiclass support:<br><br>   ================= ======================== ======================<br>   solver            l1_ratio                 multinomial multiclass<br>   ================= ======================== ======================<br>   &#x27;lbfgs&#x27;           l1_ratio=0               yes<br>   &#x27;liblinear&#x27;       l1_ratio=1 or l1_ratio=0 no<br>   &#x27;newton-cg&#x27;       l1_ratio=0               yes<br>   &#x27;newton-cholesky&#x27; l1_ratio=0               yes<br>   &#x27;sag&#x27;             l1_ratio=0               yes<br>   &#x27;saga&#x27;            0&lt;=l1_ratio&lt;=1           yes<br>   ================= ======================== ======================<br><br>.. note::<br>   &#x27;sag&#x27; and &#x27;saga&#x27; 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 &lt;Logistic_regression&gt;` for more<br>   information regarding :class:`LogisticRegression` and more specifically the<br>   :ref:`Table &lt;logistic_regression_solvers&gt;`<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 &#x27;liblinear&#x27; to &#x27;lbfgs&#x27; in 0.22.<br>.. versionadded:: 1.2<br>   newton-cholesky solver. Multinomial support in version 1.6.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">&#x27;lbfgs&#x27;</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('max_iter',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-max_iter;\"\n",
       "            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\">\n",
       "            max_iter\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-max_iter;\">\n",
       "            max_iter: int, default=100<br><br>Maximum number of iterations taken for the solvers to converge.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">100</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('verbose',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-verbose;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
       "            verbose\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-verbose;\">\n",
       "            verbose: int, default=0<br><br>For the liblinear and lbfgs solvers set verbose to any positive<br>number for verbosity.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">0</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('warm_start',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-warm_start;\"\n",
       "            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\">\n",
       "            warm_start\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-warm_start;\">\n",
       "            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 &lt;warm_start&gt;`.<br><br>.. versionadded:: 0.17<br>   *warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">False</td>\n",
       "        </tr>\n",
       "    \n",
       "\n",
       "        <tr class=\"default\">\n",
       "            <td><i class=\"copy-paste-icon\"\n",
       "                 onclick=\"copyToClipboard('n_jobs',\n",
       "                          this.parentElement.nextElementSibling)\"\n",
       "            ></i></td>\n",
       "            <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
       "            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",
       "            n_jobs\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
       "            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>\n",
       "        </a>\n",
       "    </td>\n",
       "            <td class=\"value\">None</td>\n",
       "        </tr>\n",
       "    \n",
       "                  </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    \n",
       "        <div class=\"estimator-table\">\n",
       "            <details>\n",
       "                <summary>Fitted attributes</summary>\n",
       "                <table class=\"parameters-table\">\n",
       "                    <tbody>\n",
       "                        <tr>\n",
       "                        <th>Name</th>\n",
       "                        <th>Type</th>\n",
       "                        <th>Value</th>\n",
       "                        </tr>\n",
       "                        \n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-classes_;\"\n",
       "            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\">\n",
       "            classes_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-classes_;\">\n",
       "            classes_: ndarray of shape (n_classes, )<br><br>A list of class labels known to the classifier.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int64](3,)</td>\n",
       "           <td>[0,1,2]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-coef_;\"\n",
       "            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\">\n",
       "            coef_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-coef_;\">\n",
       "            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>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](3, 2)</td>\n",
       "           <td>[[-9.33, 8.18],\n",
       " [ 3.73,-4.3 ],\n",
       " [ 5.61,-3.88]]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-intercept_;\"\n",
       "            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\">\n",
       "            intercept_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-intercept_;\">\n",
       "            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>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[float64](3,)</td>\n",
       "           <td>[ 25.2 , -6.11,-19.1 ]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.linear_model.LogisticRegression.html#:~:text=n_features_in_,-int\">\n",
       "            n_features_in_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">int</td>\n",
       "           <td>2</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "\n",
       "       <tr class=\"default\">\n",
       "           <td class=\"param\">\n",
       "        <a class=\"param-doc-link\"\n",
       "            style=\"anchor-name: --doc-link-n_iter_;\"\n",
       "            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",
       "            n_iter_\n",
       "            <span class=\"param-doc-description\"\n",
       "            style=\"position-anchor: --doc-link-n_iter_;\">\n",
       "            n_iter_: ndarray of shape (1, )<br><br>Actual number of iterations for all classes.<br><br>.. versionchanged:: 0.20<br><br>    In SciPy &lt;= 1.0.0 the number of lbfgs iterations may exceed<br>    ``max_iter``. ``n_iter_`` will now report at most ``max_iter``.</span>\n",
       "        </a>\n",
       "    </td>\n",
       "           <td class=\"fitted-att-type\">ndarray[int32](1,)</td>\n",
       "           <td>[59]</td>\n",
       "\n",
       "\n",
       "       </tr>\n",
       "    \n",
       "                    </tbody>\n",
       "                </table>\n",
       "            </details>\n",
       "        </div>\n",
       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
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      "text/plain": [
       "LogisticRegression(C=100, random_state=22)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = iris.data[:,0:2]\n",
    "y = iris.target\n",
    "lrmc = LogisticRegression( \n",
    "    solver='lbfgs',\n",
    "    C=100,\n",
    "    random_state=22\n",
    ")\n",
    "lrmc.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "319922f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "x0, x1 = np.meshgrid(\n",
    "    np.linspace(3,8,100).reshape(-1,1),\n",
    "    np.linspace(0,6,100).reshape(-1,1)\n",
    ")\n",
    "Xnew = np.c_[x0.ravel(), x1.ravel()]\n",
    "yPred = lrmc.predict_proba(Xnew)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "cfd77334",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "plt.plot(X[y==0,0], X[y==0,1],'.b',label='Setosa')\n",
    "plt.plot(X[y==1,0], X[y==1,1],'+g',label='Versi')\n",
    "plt.plot(X[y==2,0], X[y==2,1],'*m',label='Virgi')\n",
    "zz=yPred[:,1].reshape(x0.shape)\n",
    "contour=plt.contour(x0,x1,zz)\n",
    "plt.clabel(contour, inline=1,fontsize=15)\n",
    "plt.xlabel(\"Sepal Length\")\n",
    "plt.ylabel(\"Sepal Width\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "07e23e02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yPred = lrmc.predict(Xnew)\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(X[y==0,0], X[y==0,1],'bs',label='Setosa')\n",
    "plt.plot(X[y==1,0], X[y==1,1],'g^',label='Versi')\n",
    "plt.plot(X[y==2,0], X[y==2,1],'*m',label='Virgi')\n",
    "zz=yPred.reshape(x0.shape)\n",
    "contour=plt.contourf(x0,x1,zz, cmap='jet', alpha=0.3)\n",
    "plt.clabel(contour, inline=1,fontsize=15)\n",
    "plt.xlabel(\"Sepal Length\")\n",
    "plt.ylabel(\"Sepal Width\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "fbac3068",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax =plt.subplots(subplot_kw={\"projection\": \"3d\"})\n",
    "surf = ax.plot_surface(x0,x1,zz, cmap='jet')\n",
    "#ax.scatter(iris.data[:,0:1], iris.data[:,1:2], y, 'or')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.14.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}