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.

The RandomForestClassifier class has already been imported into the session.

This exercise is part of the course

Machine Learning with PySpark

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Exercise instructions

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.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create a random forest classifier
forest = ____()

# Create a parameter grid
params = ____() \
            .____(____, ['all', 'onethird', 'sqrt', 'log2']) \
            .____(____, [2, 5, 10]) \
            .____()

# Create a binary classification evaluator
evaluator = ____()

# Create a cross-validator
cv = ____(____, ____, ____, ____)

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