Exercise

Evaluating Random Forest

In this final exercise you'll be evaluating the results of cross-validation on a Random Forest model.

The following have already been created:

cv - a cross-validator which has already been fit to the training data
evaluator — a BinaryClassificationEvaluator object and
flights_test — the testing data.

Instructions

100 XP

Print a list of average AUC metrics across all models in the parameter grid.
Display the average AUC for the best model. This will be the largest AUC in the list.
Print an explanation of the maxDepth and featureSubsetStrategy parameters for the best model.
Display the AUC for the best model predictions on the testing data.