cationswhile other entries tell us how many samples of one class got mistakenly classified as another class if we declare " ninethe positive classwe can relate the entries of the confusion matrix with the terms false positive and false negative that we introduced earlier to complete the picturewe call correctly classified samples belonging to the positive class true positives and correctly classified samples belonging to the negative class true negatives these terms are usually abbreviated fpfntpand tn and lead to the following interpretation for the confusion matrix (figure - )in[ ]mglearn plots plot_binary_confusion_matrix(figure - confusion matrix for binary classification now let' use the confusion matrix to compare the models we fitted earlier (the two dummy modelsthe decision treeand the logistic regression)in[ ]print("most frequent class:"print(confusion_matrix(y_testpred_most_frequent)print("\ndummy model:"print(confusion_matrix(y_testpred_dummy)print("\ndecision tree:"print(confusion_matrix(y_testpred_tree)print("\nlogistic regression"print(confusion_matrix(y_testpred_logreg) the main diagonal of two-dimensional array or matrix is [iievaluation metrics and scoring