Exercise

Optimizing flights linear regression

Up until now you've been using the default hyper-parameters when building your models. In this exercise you'll use cross validation to choose an optimal (or close to optimal) set of model hyper-parameters.

The following have already been created:

regression — a LinearRegression object
pipeline — a pipeline with string indexer, one-hot encoder, vector assembler and linear regression and
evaluator — a RegressionEvaluator object.

Instructions

100 XP

Create a parameter grid builder.
Add grids for with regression.regParam (values 0.01, 0.1, 1.0, and 10.0) and regression.elasticNetParam (values 0.0, 0.5, and 1.0).
Build the grid.
Create a cross validator, specifying five folds.

Take Hint (-30 XP)