Best Model and Best Model Parameters

Now that we have our cross validator, cv, built out, we can tell Spark to take our data, fit the ALS algorithm to it, and try the different combinations of hyperparameter values from our param_grid so that it can identify what values provide the smallest RMSE. Unfortunately, this takes too long to complete here, but for your reference, this is how it is done:

#Fit cross validator to the 'train' dataset
model = cv.fit(train)

#Extract best model from the cv model above
best_model = model.bestModel

This code has been run separately, and the best_model has been identified and saved for you to use. Use the commands given to extract the parameters of the model.

Print type(best_model) to confirm that the model ALS built from our hyperparameter options is indeed completed. A print statement is needed here in order to work with subsequent print statements.
Extract the rank from the best_model by calling the .getRank() method on the best_model.
Extract the maxIter from the best_model by calling the .getMaxIter() method on the best_model.
Extract the regParam from the best_model by calling the .getRegParam() method on the best_model.

Exercise

Best Model and Best Model Parameters

Instructions