Common evaluation metrics

I hope the previous section gave you a nice insight of how to deal with your dataset while training ML models and also how to avoid some common pitfalls such as overfitting. In this section, we will take a brief look at some evaluation metrics that can help us judge how well our model is performing. For the purpose of all our explanations, we will assume a binary classification framework.

Before we begin, let's introduce some new terminologies. When we are dealing with a binary problem, instead of labelling the classes as 0 - 1, or even +1 - -1, we usually prefer to use the labels, positive and negative. Which of the two classes is positive is a choice that is left to the designers of the ML algorithm. Now, having said that, each prediction that our algorithm makes on the test data can be classified into the following four categories:

• True positive (TP): The data points which actually belong to the positive class, and have indeed been classified as positive by our...