Let’s try to learn from our models
26. Let’s try to learn from our models¶
In this part of the book we look a little closer at how we can interpret the results of our supervised learning models. In particular we’ll make the distinction between knowing how our models are making predictions, and whether those patterns the model finds useful are causal in some way. We’ll start off with a silly example of divorce rates and the number of waffle houses. We’ll show that the model can use the number of waffle houses to make better predictions about divorce rates, but the pattern is not causal. This is meant to help us communicate the difference between how the model is making predictions and causal interpretations to non-specialists. We’ll also take a look at a more experimental approach, called A-B testing, that tries to get at causal questions using machine learning and experimental design.