.. _sphx_glr_auto_examples_ensemble_plot_voting_decision_regions.py: ================================================== Plot the decision boundaries of a VotingClassifier ================================================== Plot the decision boundaries of a `VotingClassifier` for two features of the Iris dataset. Plot the class probabilities of the first sample in a toy dataset predicted by three different classifiers and averaged by the `VotingClassifier`. First, three exemplary classifiers are initialized (`DecisionTreeClassifier`, `KNeighborsClassifier`, and `SVC`) and used to initialize a soft-voting `VotingClassifier` with weights `[2, 1, 2]`, which means that the predicted probabilities of the `DecisionTreeClassifier` and `SVC` count 5 times as much as the weights of the `KNeighborsClassifier` classifier when the averaged probability is calculated. .. image:: /auto_examples/ensemble/images/sphx_glr_plot_voting_decision_regions_001.png :align: center .. code-block:: python print(__doc__) from itertools import product import numpy as np import matplotlib.pyplot as plt from sklearn import datasets from sklearn.tree import DecisionTreeClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.ensemble import VotingClassifier # Loading some example data iris = datasets.load_iris() X = iris.data[:, [0, 2]] y = iris.target # Training classifiers clf1 = DecisionTreeClassifier(max_depth=4) clf2 = KNeighborsClassifier(n_neighbors=7) clf3 = SVC(kernel='rbf', probability=True) eclf = VotingClassifier(estimators=[('dt', clf1), ('knn', clf2), ('svc', clf3)], voting='soft', weights=[2, 1, 2]) clf1.fit(X, y) clf2.fit(X, y) clf3.fit(X, y) eclf.fit(X, y) # Plotting decision regions 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, 0.1), np.arange(y_min, y_max, 0.1)) f, axarr = plt.subplots(2, 2, sharex='col', sharey='row', figsize=(10, 8)) for idx, clf, tt in zip(product([0, 1], [0, 1]), [clf1, clf2, clf3, eclf], ['Decision Tree (depth=4)', 'KNN (k=7)', 'Kernel SVM', 'Soft Voting']): Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape) axarr[idx[0], idx[1]].contourf(xx, yy, Z, alpha=0.4) axarr[idx[0], idx[1]].scatter(X[:, 0], X[:, 1], c=y, alpha=0.8) axarr[idx[0], idx[1]].set_title(tt) plt.show() **Total running time of the script:** (0 minutes 0.326 seconds) .. container:: sphx-glr-download **Download Python source code:** :download:`plot_voting_decision_regions.py ` .. container:: sphx-glr-download **Download IPython notebook:** :download:`plot_voting_decision_regions.ipynb `