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Evaluation metrics play an important role in achieving the optimal model during model training. It is a way to quantify the performance of the Machine learning Model. For simplicity, I'll be focusing on binary classification. But all the below metrics can be extended for multi-class classification. Let us first understand the basic building block of metrics used to evaluate a classification model. Suppose we have trained a binary image classifier model that predicts whether there is a cat or not in the image. Positive Class: Cat is a Positive class . Negative Class: No Cat is the Negative class, It can be anything(dog, horse, background, etc.) except cat in the image. Fig. 1 Fig. 2 True Positive (TP): It is an outcome when the model correctly predicts the positive class. i.e the actual image is "Cat" and the model also predicts "Cat" . False Positive (FP):   It is an outcome when the model incorrectly predicts the positive class. i.e the a...