In the realm of machine learning, evaluating the performance of a classifier is of also importance. It helps us gauge how well our model is performing and identifies areas that may require improvement means they are not making true predictions. In this chapter, we will discuss about key model evaluation metrics for classification tasks. Imagine we have a historical dataset from a telecommunication company, predicting customer churn. We’ve trained a classifier and want to assess its accuracy using the test set. Let’s explore the evaluation metrics in detail i hope you like it if you still have any question in end please ask without any hesitation.