[FIXED] How do I plot boundary decisions of multiple classifiers in one figure?

Issue

I want to plot the decision boundary conditions for multiple decision grain boundary in the same figure

The code is as follows:

import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.inspection import DecisionBoundaryDisplay
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis,QuadraticDiscriminantAnalysis
from sklearn.ensemble import AdaBoostClassifier,BaggingClassifier,RandomForestClassifier,HistGradientBoostingClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import VotingClassifier,ExtraTreesClassifier

iris = load_iris()
X = iris.data[:, :2]
classifiers = [LogisticRegression(solver='sag',penalty='l2',multi_class='ovr',
            max_iter=25000,random_state=None,fit_intercept=True),
            LinearDiscriminantAnalysis(),
            QuadraticDiscriminantAnalysis(),
            DecisionTreeClassifier(min_samples_leaf=1),
            BaggingClassifier(),
            RandomForestClassifier(),
            AdaBoostClassifier(),
            HistGradientBoostingClassifier(),
            VotingClassifier(estimators=[('rfc',RandomForestClassifier()),
                                         ('dtc',DecisionTreeClassifier())],voting ='soft'),
            ExtraTreesClassifier()]

for classifier in classifiers:
    classifier.fit(X,iris.target)
    disp = DecisionBoundaryDisplay.from_estimator(classifier, X, response_method="predict", xlabel=iris.feature_names[0], ylabel=iris.feature_names[1], alpha=0.5)
    disp.ax_.scatter(X[:, 0], X[:, 1], c=iris.target, edgecolor="k")

plt.show()

I did not get the result that I want, I need those plots in the same figure.

Can someone help me in this case?

Solution

To get the decision boundaries of different classifiers in one figure, make sure to pass the argument ax in DecisionBoundaryDisplay.from_estimator:

# Assuming there are 10 classifiers
fig, ax = plt.subplots(nrows=5, ncols=2)
ax = ax.T.flatten()
i = 0

for classifier in classifiers:

    classifier.fit(X,iris.target)
    disp = DecisionBoundaryDisplay.from_estimator(classifier, 
    X, response_method="predict", 
    xlabel=iris.feature_names[0], ylabel=iris.feature_names[1],
    alpha=0.5, ax=ax[i])

    disp.ax_.scatter(X[:, 0], X[:, 1], c=iris.target, edgecolor="k")
    
    # Refer to next Axes in a 5 * 2 grid created by plt.subplots
    i += 1
    
    # To get labels for contour plots
    labels = np.unique(iris.target)
    proxy = [plt.Rectangle((0,0),1,1,fc = pc.get_facecolor()[0]) for pc in disp.surface_.collections]
    disp.ax_.legend(proxy, labels)

plt.show()

This gives:

This answer was motivated by this answer and this answer.

Answered By - medium-dimensional