Data Science Projects with Python - Second Edition

4. The Bias-Variance Trade-Off

Overview

In this chapter, we'll cover the remaining elements of logistic regression, including what happens when you call .fit to train the model, and the statistical assumptions you should be aware of when using this modeling technique. You will learn how to use L1 and L2 regularization with logistic regression to prevent overfitting and how to use the practice of cross-validation to decide the regularization strength. After reading this chapter, you will be able to use logistic regression in your work and employ regularization in the model fitting process to take advantage of the bias-variance trade-off and improve model performance on unseen data.

Introduction

In this chapter, we will introduce the remaining ...