Choosing the Right Metric for Evaluating Machine Learning Models -- Part 2

In the first blog, we discussed some important metrics used in regression, their pros and cons, and use cases. This part will focus on commonly used metrics in classification, why should we prefer some over others with context. Let's first understand the basic terminology used in classification problems before going through the pros and cons of each method. You can skip this section if you are already familiar with the terminology. The probabilistic interpretation of ROC-AUC score is that if you randomly choose a positive case and a negative case, the probability that the positive case outranks the negative case according to the classifier is given by the AUC.