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Exercise

Delayed flights with a Random Forest

In this exercise you'll bring together cross validation and ensemble methods. You'll be training a Random Forest classifier to predict delayed flights, using cross validation to choose the best values for model parameters.

You'll find good values for the following parameters:

  • featureSubsetStrategy — the number of features to consider for splitting at each node and
  • maxDepth — the maximum number of splits along any branch.

Unfortunately building this model takes too long, so we won't be running the .fit() method on the pipeline.

Instructions

100 XP
  • Create a random forest classifier object.
  • Create a parameter grid builder object. Add grid points for the featureSubsetStrategy and maxDepth parameters.
  • Create binary classification evaluator.
  • Create a cross-validator object, specifying the estimator, parameter grid and evaluator. Choose 5-fold cross validation.