File Content: plot_iris.py """ ================================================================ Plot the decision surface of a decision tree on the iris dataset ================================================================ Plot the decision surface of a decision tree trained on pairs of features of the iris dataset. See :ref:`decision tree <tree>` for more information on the estimator. For each pair of iris features, the decision tree learns decision boundaries made of combinations of simple thresholding rules inferred from the training samples. """ print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import load_iris from sklearn.tree import DecisionTreeClassifier # Parameters n_classes = 3 plot_colors = "bry" plot_step = 0.02 # Load data iris = load_iris() for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]]): # We only take the two corresponding features X = iris.data[:, pair] y = iris.target # Train clf = DecisionTreeClassifier().fit(X, y) # Plot the decision boundary plt.subplot(2, 3, pairidx + 1) x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step), np.arange(y_min, y_max, plot_step)) Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Paired) plt.xlabel(iris.feature_names[pair[0]]) plt.ylabel(iris.feature_names[pair[1]]) plt.axis("tight") # Plot the training points for i, color in zip(range(n_classes), plot_colors): idx = np.where(y == i) plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i], cmap=plt.cm.Paired) plt.axis("tight") plt.suptitle("Decision surface of a decision tree using paired features") plt.legend() plt.show() Submit