Plot multiple confusion matrices with plot_confusion_matrix

Let's use the good'ol iris dataset to reproduce this, and fit several classifiers to plot their respective confusion matrices with plot_confusion_matrix:

from sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import plot_confusion_matrix

data = load_iris()
X = data.data
y = data.target

Set up -

X_train, X_test, y_train, y_test = train_test_split(X, y)
classifiers = [LogisticRegression(solver='lbfgs'), 
               AdaBoostClassifier(),
               GradientBoostingClassifier(), 
               SVC()]
for cls in classifiers:
    cls.fit(X_train, y_train)

So the way you could compare all matrices at simple sight, is by creating a set of subplots with plt.subplots. Then iterate both over the axes objects and the trained classifiers (plot_confusion_matrix expects the as input) and plot the individual confusion matrices:

fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(15,10))

for cls, ax in zip(classifiers, axes.flatten()):
    plot_confusion_matrix(cls, 
                          X_test, 
                          y_test, 
                          ax=ax, 
                          cmap='Blues',
                         display_labels=data.target_names)
    ax.title.set_text(type(cls).__name__)
plt.tight_layout()  
plt.show()

if your desired output is that This is my way to see multiple confusion matrices (confusion_matrix) side by side with ConfusionMatrixDisplay.

note: paste your own test and train data names in "metrics.confusion_matrix()" function.

    fig, ax = plt.subplots(1,2)
    ax[0].set_title("test")
    ax[1].set_title("train")

    metrics.ConfusionMatrixDisplay(
    confusion_matrix = metrics.confusion_matrix(y_test, y_pred), 
    display_labels = [False, True]).plot(ax=ax[0])

    metrics.ConfusionMatrixDisplay(
    confusion_matrix = metrics.confusion_matrix(y_train, y_train_pred), 
    display_labels = [False, True]).plot(ax=ax[1]);