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

Question

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?

Answer 1

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.