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()