Introduction

In the previous three chapters, we discussed the two types of supervised learning problems, regression and classification, followed by ensemble models, which were built from a combination of base models. We built several models and discussed how and why they work.

However, that is not enough to take a model to production. Model development is an iterative process, and the model training step is followed by validation and updating steps:

Figure 6.1: Machine learning model development process

This chapter will explain the peripheral steps in the process shown in the preceding flowchart; we will discuss how to select the appropriate hyperparameters and how to perform model validation using the appropriate error metrics. Improving a model's performance happens by iteratively performing these two tasks.

But why is it important to evaluate your model? Say you've trained your model and provided some hyperparameters, made predictions, and found its accuracy. That's the gist of it, but how...