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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
each count 2 times
as much as the weights of the KNeighborsClassifier
classifier when the averaged probability is calculated.
# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause
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()
Total running time of the script: (0 minutes 0.668 seconds)
Related examples
Plot class probabilities calculated by the VotingClassifier
Plot the decision surface of decision trees trained on the iris dataset
Plot multi-class SGD on the iris dataset
Plot different SVM classifiers in the iris dataset