Chapter 6

Model Evaluation

Learning Objectives

By the end of this chapter, you will be able to:

  • Explain the importance of evaluating models
  • Evaluate regression and classification models using a number of metrics
  • Choose the right metric for evaluating and tuning a model
  • Explain the importance of hold-out datasets and types of sampling
  • Perform hyperparameter tuning to find the best model
  • Calculate feature importance and explain why they are important

This chapter introduces us to how we can improve a model's performance by using hyperparameters and model evaluation metrics.

Introduction

In the previous three chapters, we discussed the two types of supervised learning problems, regression and classification, followed by ensemble models, ...

Get Applied Supervised Learning with Python now with O’Reilly online learning.

O’Reilly members experience live online training, plus books, videos, and digital content from 200+ publishers.