The Supervised Learning Workshop

7. Model Evaluation

Overview

This chapter is an introduction to how you can improve a model's performance by using hyperparameters and model evaluation metrics. You will see how to evaluate regression and classification models using a number of metrics and learn how to choose a suitable metric for evaluating and tuning a model.

By the end of this chapter, you will be able to implement various sampling techniques and perform hyperparameter tuning to find the best model. You will also be well equipped to calculate feature importance for model evaluation.

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 ...