.. _sphx_glr_auto_examples_ensemble_plot_voting_probas.py: =========================================================== Plot class probabilities calculated by the VotingClassifier =========================================================== Plot the class probabilities of the first sample in a toy dataset predicted by three different classifiers and averaged by the `VotingClassifier`. First, three examplary classifiers are initialized (`LogisticRegression`, `GaussianNB`, and `RandomForestClassifier`) and used to initialize a soft-voting `VotingClassifier` with weights `[1, 1, 5]`, which means that the predicted probabilities of the `RandomForestClassifier` count 5 times as much as the weights of the other classifiers when the averaged probability is calculated. To visualize the probability weighting, we fit each classifier on the training set and plot the predicted class probabilities for the first sample in this example dataset. .. image:: /auto_examples/ensemble/images/sphx_glr_plot_voting_probas_001.png :align: center .. code-block:: python print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn.linear_model import LogisticRegression from sklearn.naive_bayes import GaussianNB from sklearn.ensemble import RandomForestClassifier from sklearn.ensemble import VotingClassifier clf1 = LogisticRegression(random_state=123) clf2 = RandomForestClassifier(random_state=123) clf3 = GaussianNB() X = np.array([[-1.0, -1.0], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]]) y = np.array([1, 1, 2, 2]) eclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('gnb', clf3)], voting='soft', weights=[1, 1, 5]) # predict class probabilities for all classifiers probas = [c.fit(X, y).predict_proba(X) for c in (clf1, clf2, clf3, eclf)] # get class probabilities for the first sample in the dataset class1_1 = [pr[0, 0] for pr in probas] class2_1 = [pr[0, 1] for pr in probas] # plotting N = 4 # number of groups ind = np.arange(N) # group positions width = 0.35 # bar width fig, ax = plt.subplots() # bars for classifier 1-3 p1 = ax.bar(ind, np.hstack(([class1_1[:-1], [0]])), width, color='green') p2 = ax.bar(ind + width, np.hstack(([class2_1[:-1], [0]])), width, color='lightgreen') # bars for VotingClassifier p3 = ax.bar(ind, [0, 0, 0, class1_1[-1]], width, color='blue') p4 = ax.bar(ind + width, [0, 0, 0, class2_1[-1]], width, color='steelblue') # plot annotations plt.axvline(2.8, color='k', linestyle='dashed') ax.set_xticks(ind + width) ax.set_xticklabels(['LogisticRegression\nweight 1', 'GaussianNB\nweight 1', 'RandomForestClassifier\nweight 5', 'VotingClassifier\n(average probabilities)'], rotation=40, ha='right') plt.ylim([0, 1]) plt.title('Class probabilities for sample 1 by different classifiers') plt.legend([p1[0], p2[0]], ['class 1', 'class 2'], loc='upper left') plt.show() **Total running time of the script:** (0 minutes 0.235 seconds) .. container:: sphx-glr-download **Download Python source code:** :download:`plot_voting_probas.py ` .. container:: sphx-glr-download **Download IPython notebook:** :download:`plot_voting_probas.ipynb `