PYTHON

sklearn plot confusion matrix

import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix, plot_confusion_matrix

clf = # define your classifier (Decision Tree, Random Forest etc.)
clf.fit(X, y) # fit your classifier

# make predictions with your classifier
y_pred = clf.predict(X)         
# optional: get true negative (tn), false positive (fp)
# false negative (fn) and true positive (tp) from confusion matrix
M = confusion_matrix(y, y_pred)
tn, fp, fn, tp = M.ravel() 
# plotting the confusion matrix
plot_confusion_matrix(clf, X, y)
plt.show()

import sklearn.metrics from plot_confusion_matrix

from sklearn.metrics import plot_confusion_matrix

sklearn plot confusion matrix

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import  plot_confusion_matrix
clf = LogisticRegression()
clf.fit(X_train,y_train)
disp = plot_confusion_matrix(clf,X_test,y_test,cmap="Blues",values_format='.3g')
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')

plot confusion matrix scikit learn

from sklearn import metrics
metrics.ConfusionMatrixDisplay.from_predictions(true_y, predicted_y).plot()