Classification metrics and their Use Cases
In this blog, we will discuss about commonly used classification metrics. We will be covering Accuracy Score , Confusion Matrix , Precision , Recall , F-Score , ROC-AUC and will then learn how to extend them to the multi-class classification . We will also discuss in which scenarios, which metric will be most suitable to use. First let’s understand some important terms used throughout the blog- True Positive (TP): When you predict an observation belongs to a class and it actually does belong to that class. True Negative (TN): When you predict an observation does not belong to a class and it actually does not belong to that class. False Positive (FP) : When you predict an observation belongs to a class and it actually does not belong to that class. False Negative(FN): When you predict an observation does not belong to a class and it actually does belong to that class. All classification metrics work on these four terms. Let’s start understanding classification metrics- Accu