Models: Interpretability, Accuracy, and Calibration

In this chapter, we present important concepts for when dealing with predictive models. We start with a discussion about the interpretability and explainability of models and algorithms, presenting different tools that could help us to understand “why” the predicted outcome of the model is the one we got. Then, we will discuss accuracy, which is usually the ultimate target of most machine-learning techniques. But as we see, the most important concept is the “good calibration” of the model, which means that we want to have, locally, a balanced portfolio, and that the probability predicted by the model is, indeed, related to the true risk.