In the previous chapter, you learned about model assessment using various metrics such as R2 score, MAE, and accuracy. These metrics help you decide which models to keep and which ones to discard. In this chapter, you will learn some more techniques for training better models.
Generalization deals with getting your models to perform well enough on data points that they have not encountered in the past (that is, during training). We will address two specific areas:
- How to make use of as much of your data as possible to train a model
- How to reduce overfitting in a model