are interested in detecting or predicting when the patient does not have breast cancer (benignthen label is our positive class howeversuppose our class of interest was to detect cancer (malignantthen we could have chosen label as our positive class figure - shows typical confusion matrix for binary classification problemwhere denotes the positive class and denotes the negative class figure - typical structure of confusion matrix figure - should make things more clear with regard to the structure of confusion matrices in generalwe usually have positive class as we discussed earlier and the other class is the negative class based on this structurewe can clearly see four terms of importance true positive (tp)this is the count of the total number of instances from the positive class where the true class label was equal to the predicted class labeli the total instances where we correctly predicted the positive class label with our model false positive (fp)this is the count of the total number of instances from the negative class where our model misclassified them by predicting them as positive hence the namefalse positive true negative (fn)this is the count of the total number of instances from the negative class where the true class label was equal to the predicted class labeli the total instances where we correctly predicted the negative class label with our model false negative (fn)this is the count of the total number of instances from the positive class where our model misclassified them by predicting them as negative hence the namefalse negative