Summary
In this chapter, we learned a lot of important things that we need to know regarding how to evaluate ML models properly. Starting from the concept of overfitting, numerous data splitting strategies, how to choose the best data splitting strategy based on the given situation, and how to implement each of them using the Scikit-Learn package. Understanding these concepts is important since you can't perform a good hyperparameter tuning process without applying the appropriate data splitting strategy.
In the next chapter, we will discuss hyperparameter tuning. We will not only discuss the definition but also several misconceptions and types of hyperparameter distributions.