Introduction

As we've seen in the previous chapters, it's easy to train models with scikit-learn using just a few lines of Python code. This is possible by abstracting away the computational complexity of the algorithm, including details such as constructing cost functions and optimizing model parameters. In other words, we deal with a black box where the internal operations are hidden from us.

While the simplicity offered by this approach is quite nice on the surface, it does nothing to prevent the misuse of algorithms—for example, by selecting the wrong model for a dataset, overfitting on the training set, or failing to test properly on unseen data.

In this chapter, we'll show you how to avoid some of these pitfalls while training classification models and equip you with the tools to produce trustworthy results. We'll introduce k-fold cross validation and validation curves, and then look at ways to use them in Jupyter.

We'll also introduce...