Database Reference
In-Depth Information
Summary
In this chapter, you saw how to use MLlib's linear model and decision tree functionality in
Python 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 imple-
ment 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 unsuper-
vised learning, specifically in clustering models.
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