Introduction

In the previous chapter, you saw how to find the optimal hyperparameters of some of the most popular Machine Learning algorithms in order to get better predictive performance (that is, more accurate predictions).

Machine Learning algorithms are always referred to as black box where we can only see the inputs and outputs and the implementation inside the algorithm is quite opaque, so people don't know what is happening inside.

With each day that passes, we can sense the elevated need for more transparency in Machine Learning models. In the last few years, we have seen some cases where algorithms have been accused of discriminating against groups of people. For instance, a few years ago, a not-for-profit news organization called ProPublica highlighted bias in the COMPAS algorithm, built by the Northpointe company. The objective of the algorithm is to assess the likelihood of re-offending for a criminal. It was shown that the algorithm was predicting a higher...