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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
  • Retrieve a list of average AUC metrics across all models in the parameter grid.
  • What is the average AUC for the best model? This will be the largest AUC in the list.
  • Find the value of the maxDepth and featureSubsetStrategy parameters for the best model.
  • Calculate the AUC for the best model predictions on the testing data.
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