Introduction

In the previous chapter, we explored a few strategies that helped us build improved models using feature selection and dimensionality reduction. These strategies primarily focus on improving the model's computational performance and interpretability; however, to improve the model's performance with respect to performance metrics, such as overall accuracy or error estimates to build robust and more generalized models, we will need to focus on cross-validation and hyperparameter tuning.

In this chapter, we will walk you through the fundamental topics in machine learning to build generalized and robust models using cross-validation and hyperparameter tuning and implement them in R.

We will first study the topics in this chapter in detail with layman examples and leverage simple use cases to see the implementation in action.