This is a follow up article on "The Importance of F1 Score" in which we understood the technical aspects of evaluating a Machine Learning model. In this article we will understand how different evaluation metrics can help us in designing solutions based on the problem statement and domain. I will like to distinguish evaluation metrics with respect to the following criteria: When there is life involved : In case of aircraft or ships, where a Machine Learning algorithm misses to detect a failure can be a costly affair, since there is life involved. Also in the case of detecting cancer, a failure to predict a true positive can lead to delay in treatment, and hence can be life threatening. In cases such as detecting failures in aircrafts, even if we get false positives is acceptable, but not able to detect true positives can be too expensive in terms of loss of life. Thus it becomes important to use Recall as a measure, as any positive detection becomes important, even...