Introduction

In the previous chapter, we developed a few example machine learning models using scikit-learn, to get familiar with how it works. However, the features we used, EDUCATION and LIMIT_BAL, were not chosen in a systematic way.

In this chapter, we will start to develop techniques that can be used to assess features for their usefulness in modeling. This will enable you to make a quick pass over all candidate features, to have an idea of which will be the most important. For the most promising features, we will see how to create visual summaries that serve as useful communication tools.

Next, we will begin our detailed examination of logistic regression. We'll learn why logistic regression is considered to be a linear model, even if the formulation involves some non-linear functions. We'll learn what a decision boundary is and see that as a key consequence of its linearity, the decision boundary of logistic regression could make it difficult to accurately classify...