Evaluation Metrics

Evaluating a machine learning model is an essential part of any project: once we have allowed our model to learn from the training data, the next step is to measure the performance of the model. We need to find a metric that can not only tell us how accurate the predictions made by the model are, but also allow us to compare the performance of a number of models so that we can select the one best suited for our use case.

Defining a metric is usually one of the first things we should do when defining our problem statement and before we begin the EDA, since it's a good idea to plan ahead and think about how we intend to evaluate the performance of any model we build and how to judge whether it is performing optimally or not. Eventually, calculating the performance evaluation metric will fit into the machine learning pipeline.

Needless to say, evaluation metrics will be different for regression tasks and classification tasks, since the output values in the former are continuous...