# Introduction

In this chapter, we will introduce the remaining details of logistic regression left over from the previous chapter. In addition to being able to use scikit-learn to fit logistic regression models, you will gain insight into the gradient descent procedure, which is similar to the processes that are used "under the hood" (invisible to the user) to accomplish model fitting in scikit-learn. Finally, we'll complete our discussion of the logistic regression model by familiarizing ourselves with the formal statistical assumptions of this method.

We begin our exploration of the foundational machine learning concepts of overfitting, underfitting, and the bias-variance trade-off by examining how the logistic regression model can be extended to address the overfitting problem. After reviewing the mathematical details of the regularization methods that are used to alleviate overfitting, you will learn a useful practice for tuning the hyperparameters of regularization...