Note
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Show below is a logistic-regression classifiers decision boundaries on the first two dimensions (sepal length and width) of the iris dataset. The datapoints are colored according to their labels.
print(__doc__) # Code source: Gaël Varoquaux # Modified for documentation by Jaques Grobler # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from sklearn.linear_model import LogisticRegression from sklearn import datasets # import some data to play with iris = datasets.load_iris() X = iris.data[:, :2] # we only take the first two features. Y = iris.target logreg = LogisticRegression(C=1e5, solver='lbfgs', multi_class='multinomial') # we create an instance of Neighbours Classifier and fit the data. logreg.fit(X, Y) # Plot the decision boundary. For that, we will assign a color to each # point in the mesh [x_min, x_max]x[y_min, y_max]. x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5 y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5 h = .02 # step size in the mesh xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) Z = logreg.predict(np.c_[xx.ravel(), yy.ravel()]) # Put the result into a color plot Z = Z.reshape(xx.shape) plt.figure(1, figsize=(4, 3)) plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired) # Plot also the training points plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors='k', cmap=plt.cm.Paired) plt.xlabel('Sepal length') plt.ylabel('Sepal width') plt.xlim(xx.min(), xx.max()) plt.ylim(yy.min(), yy.max()) plt.xticks(()) plt.yticks(()) plt.show()
Total running time of the script: ( 0 minutes 0.103 seconds)
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http://scikit-learn.org/stable/auto_examples/linear_model/plot_iris_logistic.html