71 lines
2.2 KiB
Python
71 lines
2.2 KiB
Python
|
"""
|
||
|
|
==================================================
|
||
|
|
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()
|