In this chapter, you saw how to use ML Library's linear model, decision tree, gradient boosted trees, Ridge Regression, and the isotonic regression functionality in Scala within the context of regression models. We explored categorical feature extraction, and the impact of applying transformations to the target variable in a regression problem. Finally, we implemented various performance-evaluation metrics, and used them to implement a cross-validation exercise that explores the impact of the various parameter settings available in both linear models and decision trees on test set model performance.
In the next chapter, we will cover a different approach to machine learning, that is, unsupervised learning, specifically in clustering models.