Introduction

In the previous chapters, we discussed the two types of supervised learning problems, regression and classification, followed by ensemble models, which are 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, as shown in the following figure:

Figure 7.1: 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...