Summary

In this chapter, we studied the importance of withholding some of the available data to evaluate models. We also learned how to make use of all of the available data with a technique called cross-validation to find the best performing model from a set of models you are training. We also made use of evaluation metrics to determine when a model starts to overfit and made use of ridge and lasso regression to fix a model that is overfitting.

In the next chapter, we will go into hyperparameter tuning in depth. You will learn about various techniques for finding the best hyperparameters to train your models.