This chapter, along with the next chapter, covers the fundamental techniques for regression and classification available in Spark 2.0 ML and MLlib library. Spark 2.0 highlights a new direction by moving the RDD-based regressions (see the next chapter) to maintenance mode while emphasizing Linear Regression and Generalized Regression going forward.
At a high level, the new API design favors parameterization of elastic net to produce the ridge versus Lasso regression and everything in between, as opposed to a named API (for example, LassoWithSGD). The new API approach is a much cleaner design and forces you to learn elastic net and its power when it comes to feature engineering that remains an art in data science. We provide adequate ...
