# 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

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

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 0.639 seconds)

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