How do I plot boundary decisions of multiple classifiers in one figure?-CodePudding

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?

CodePudding user response：

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

This answer was motivated by this answer and this answer.