Summary

In this chapter, we have learned how to explore features one at a time, using univariate feature selection methods including Pearson correlation and an ANOVA F-test. While looking at features in this way does not always tell the whole story, since you are potentially missing out on important interactions between features, it is often a helpful step. Understanding the relationships between the most predictive features and the response variable, and creating effective visualizations around them, is a great way to communicate your findings to your client. We used customized plots, such as overlapping histograms created with Matplotlib, to create visualizations of the most important features.

Then we began an in-depth description of how logistic regression works, exploring such topics as the sigmoid function, log odds, and the linear decision boundary. While logistic regression is one of the simplest classification models, and often is not as powerful as other methods, it is...