Chapter 5. Making Decisions Black and White with Logistic Regression

In the last chapter, we used regression to predict values over a continuous range. In this chapter, we will explore the tuning of a regression model that predicts a binary classification. You are probably already pretty familiar with this method, so we'll spend just time introducing the aspects that we'll be leveraging.

The most important thing about logistic regression is that its form is very different from linear regression. Likewise, interpreting the results is also different and quite confusing. A standard N-variable logistic regression model has the following form:

While in linear regression, the beta coefficient represents the change for every unit of change in the associated x variable. In logistic regression, the betas represent the change in log-odds for every unit's increase in the associated x variable. As a result of this very difference in the model, the way in which we generate data will need to be changed...