Studying intrinsically interpretable (white-box) models

So far, in this chapter, we have already fitted our training data to model classes representing each of these "white-box" model families. The purpose of this section is to show you exactly why they are intrinsically interpretable. We'll do so by employing the models that were previously fitted.

Generalized Linear Models (GLMs)

GLMs are a large family of model classes that have a model for every statistical distribution. Just like linear regression assumes your target feature and residuals have a normal distribution, logistic regression assumes the Bernoulli distribution. There are GLMs for every distribution, such as Poisson regression for Poisson distribution and multinomial response for multinomial distribution. You choose which GLM to use based on the distribution of your target variable and whether your data meets the other assumptions of the GLM (they vary). In addition to an underlying distribution...