```python from sklearn import datasets iris = datasets.load_iris() print(iris.DESCR) ``` .. _iris_dataset: Iris plants dataset -------------------- **Data Set Characteristics:** :Number of Instances: 150 (50 in each of three classes) :Number of Attributes: 4 numeric, predictive attributes and the class :Attribute Information: - sepal length in cm - sepal width in cm - petal length in cm - petal width in cm - class: - Iris-Setosa - Iris-Versicolour - Iris-Virginica :Summary Statistics: ============== ==== ==== ======= ===== ==================== Min Max Mean SD Class Correlation ============== ==== ==== ======= ===== ==================== sepal length: 4.3 7.9 5.84 0.83 0.7826 sepal width: 2.0 4.4 3.05 0.43 -0.4194 petal length: 1.0 6.9 3.76 1.76 0.9490 (high!) petal width: 0.1 2.5 1.20 0.76 0.9565 (high!) ============== ==== ==== ======= ===== ==================== :Missing Attribute Values: None :Class Distribution: 33.3% for each of 3 classes. :Creator: R.A. Fisher :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov) :Date: July, 1988 The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken from Fisher's paper. Note that it's the same as in R, but not as in the UCI Machine Learning Repository, which has two wrong data points. This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day. (See Duda & Hart, for example.) The data set contains 3 classes of 50 instances each, where each class refers to a type of iris plant. One class is linearly separable from the other 2; the latter are NOT linearly separable from each other. |details-start| **References** |details-split| - Fisher, R.A. "The use of multiple measurements in taxonomic problems" Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions to Mathematical Statistics" (John Wiley, NY, 1950). - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis. (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218. - Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System Structure and Classification Rule for Recognition in Partially Exposed Environments". IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-2, No. 1, 67-71. - Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule". IEEE Transactions on Information Theory, May 1972, 431-433. - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al"s AUTOCLASS II conceptual clustering system finds 3 classes in the data. - Many, many more ... |details-end| ```python import matplotlib.pyplot as plt import numpy as np ``` ```python sl = iris.data[:,0:1] sw = iris.data[:,1:2] plt.plot(sl,sw, '.k') plt.show() ``` ![png](main_files/main_2_0.png) ```python iris.target ``` array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) # Decision boundaries ```python import numpy as np import matplotlib.pyplot as plt t = np.linspace(-10,10, 100) sig = 1/(1+np.exp(-t-4)) plt.plot(t,sig, '.b', label=r"$\sigma$") plt.legend(loc='upper left', fontsize =20) plt.show() ``` ![png](main_files/main_5_0.png) # Iris-Setosa Classifier based on petal width ```python X = iris.data[:,3:4] y = (iris.target == 0).astype(int) ``` ```python from sklearn.linear_model import LogisticRegression mylr = LogisticRegression(solver='lbfgs', random_state=42) mylr.fit(X,y) ``` 
LogisticRegression(random_state=42)
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```python Xnew = np.linspace(-1,3,100).reshape(-1,1) yPred = mylr.predict_proba(Xnew) #plt.plot(Xnew,yPred[:,0], label='No Iris') plt.plot(Xnew,yPred[:,1], label='Yes Iris') plt.legend() plt.plot(X,y,'og') plt.show() ``` ![png](main_files/main_9_0.png) # Iris-Setosa petal length ```python X = iris.data[:,2:3] y = (iris.target == 0).astype(int) from sklearn.linear_model import LogisticRegression mylr = LogisticRegression(solver='lbfgs', random_state=42) mylr.fit(X,y) ``` 
LogisticRegression(random_state=42)
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```python Xnew = np.linspace(0,8,100).reshape(-1,1) yPred = mylr.predict_proba(Xnew) #plt.plot(Xnew,yPred[:,0], label='No Iris') plt.plot(Xnew,yPred[:,1], label='Yes Iris') plt.legend() plt.plot(X,y,'og') plt.axis([1.5, 5, -0.1, 1.1]) plt.show() ``` ![png](main_files/main_12_0.png) # Iris-Setosa Sepal-Length ```python X = iris.data[:,0:1] y = (iris.target == 0).astype(int) from sklearn.linear_model import LogisticRegression mylr = LogisticRegression(solver='lbfgs', random_state=42) mylr.fit(X,y) ``` 
LogisticRegression(random_state=42)
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```python Xnew = np.linspace(0,8,100).reshape(-1,1) yPred = mylr.predict_proba(Xnew) #plt.plot(Xnew,yPred[:,0], label='No Iris') plt.plot(Xnew,yPred[:,1], label='Yes Iris') plt.legend() plt.plot(X,y,'og') plt.axis([3.5, 7, -0.1, 1.1]) plt.show() ``` ![png](main_files/main_15_0.png) # Multiple features classifier ```python import matplotlib.pyplot as plt sl = iris.data[:,0:1] sw = iris.data[:,1:2] tg = iris.target plt.plot(sl[tg==0,0], sw[tg==0,0],'.g' ,label='Set') plt.plot(sl[tg==1,0], sw[tg==1,0],'.r', label='Ver') plt.plot(sl[tg==2,0], sw[tg==2,0],'.b', label='Vir') plt.legend() plt.show() ``` ![png](main_files/main_17_0.png) ```python from sklearn.linear_model import LogisticRegression X = iris.data[:,0:2] y = (iris.target==2).astype(int) ``` ```python mylrvir = LogisticRegression( random_state=22, tol=1e-5, C=100, max_iter=100, solver='newton-cg' ) mylrvir.fit(X,y) ``` 
LogisticRegression(C=100, random_state=22, solver='newton-cg', tol=1e-05)
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```python import numpy as np x0, x1 = np.meshgrid( np.linspace(3,8,100).reshape(-1,1), np.linspace(0,6,100).reshape(-1,1) ) Xnew = np.c_[x0.ravel(), x1.ravel()] yPred = mylrvir.predict_proba(Xnew) ``` ```python plt.figure(figsize=(10,4)) plt.plot(X[y==0,0], X[y==0,1],'bs',label='No Virg') plt.plot(X[y==1,0], X[y==1,1],'g^',label='Virginica') zz=yPred[:,1].reshape(x0.shape) contour=plt.contour(x0,x1,zz) plt.clabel(contour, inline=1,fontsize=15) plt.xlabel("Sepal Length") plt.ylabel("Sepal Width") plt.legend() plt.show() ``` ![png](main_files/main_21_0.png) ```python fig, ax =plt.subplots(subplot_kw={"projection": "3d"}) surf = ax.plot_surface(x0,x1,zz, cmap='jet') ax.scatter(iris.data[:,0:1], iris.data[:,1:2], y, 'or') ``` ![png](main_files/main_22_1.png) # Multiple features and muticlass classifier ```python X = iris.data[:,0:2] y = iris.target lrmc = LogisticRegression( multi_class='multinomial', solver='lbfgs', C=100, random_state=22 ) lrmc.fit(X,y) ``` 
LogisticRegression(C=100, multi_class='multinomial', random_state=22)
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```python y ``` array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]) ```python import numpy as np x0, x1 = np.meshgrid( np.linspace(3,8,100).reshape(-1,1), np.linspace(0,6,100).reshape(-1,1) ) Xnew = np.c_[x0.ravel(), x1.ravel()] yPred = lrmc.predict_proba(Xnew) ``` ```python plt.figure(figsize=(10,4)) plt.plot(X[y==0,0], X[y==0,1],'.b',label='Setosa') plt.plot(X[y==1,0], X[y==1,1],'+g',label='Versi') plt.plot(X[y==2,0], X[y==2,1],'*m',label='Virgi') zz=yPred[:,1].reshape(x0.shape) contour=plt.contour(x0,x1,zz) plt.clabel(contour, inline=1,fontsize=15) plt.xlabel("Sepal Length") plt.ylabel("Sepal Width") plt.legend() plt.show() ``` ![png](main_files/main_27_0.png) ```python yPred = lrmc.predict(Xnew) plt.figure(figsize=(10,6)) plt.plot(X[y==0,0], X[y==0,1],'bs',label='Setosa') plt.plot(X[y==1,0], X[y==1,1],'g^',label='Versi') plt.plot(X[y==2,0], X[y==2,1],'*m',label='Virgi') zz=yPred.reshape(x0.shape) contour=plt.contourf(x0,x1,zz, cmap='jet', alpha=0.3) plt.clabel(contour, inline=1,fontsize=15) plt.xlabel("Sepal Length") plt.ylabel("Sepal Width") plt.legend() plt.show() ``` ![png](main_files/main_28_0.png) ```python fig, ax =plt.subplots(subplot_kw={"projection": "3d"}) surf = ax.plot_surface(x0,x1,zz, cmap='jet') #ax.scatter(iris.data[:,0:1], iris.data[:,1:2], y, 'or') ``` ![png](main_files/main_29_0.png) ```python ```