Introduction

When you assess the performance of a model, you look at certain measurements or values that tell you how well the model is performing under certain conditions, and that helps you make an informed decision about whether or not to make use of the model that you have trained in the real world. Some of the measurements you will encounter in this chapter are MAE, precision, recall, and R2 score.

You learned how to train a regression model in Chapter 2, Regression, and how to train classification models in Chapter 3, Binary Classification. Consider the task of predicting whether or not a customer is likely to purchase a term deposit, which you addressed in Chapter 3, Binary Classification. You have learned how to train a model to perform this sort of classification. You are now concerned with how useful this model might be. You might start by training one model, and then evaluating how often the predictions from that model are correct. You might then proceed to train more...