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"""
==================================================
Plot the decision boundaries of a VotingClassifier
==================================================
.. currentmodule:: sklearn
Plot the decision boundaries of a :class:`~ensemble.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
:class:`~ensemble.VotingClassifier`.
First, three exemplary classifiers are initialized
(:class:`~tree.DecisionTreeClassifier`,
:class:`~neighbors.KNeighborsClassifier`, and :class:`~svm.SVC`) and used to
initialize a soft-voting :class:`~ensemble.VotingClassifier` with weights `[2,
1, 2]`, which means that the predicted probabilities of the
:class:`~tree.DecisionTreeClassifier` and :class:`~svm.SVC` each count 2 times
as much as the weights of the :class:`~neighbors.KNeighborsClassifier`
classifier when the averaged probability is calculated.
"""
from itertools import product
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.ensemble import VotingClassifier
from sklearn.inspection import DecisionBoundaryDisplay
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
# 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(gamma=0.1, 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
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"],
):
DecisionBoundaryDisplay.from_estimator(
clf, X, alpha=0.4, ax=axarr[idx[0], idx[1]], response_method="predict"
)
axarr[idx[0], idx[1]].scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor="k")
axarr[idx[0], idx[1]].set_title(tt)
plt.show()
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