CONFUSION MATRIX

Accuracy: Of all the classes, how many you predicted right. Accuracy is simply the fraction of the total sample that is correctly identified. Precision: Out of all the classes we have predicted as positive, how many are actually positive. Precision is very useful when you have a model that starts some kind of business workflow (e.g. So, you want your model to be as correct as possible when it says 1 and don't care too much when it predicts 0. That's why we see only the second column of the confusion matrix, which is related to a prediction equal to 1. Precision is very used in marketing campaigns, because a marketing automation campaign is supposed to start an activity on a user when it predicts that they will respond successfully.