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Build your cross validation pipeline

Now that we have our data, our train/test splits, our model, and our hyperparameter values, let's tell Spark how to cross validate our model so it can find the best combination of hyperparameters and return it to us.

This exercise is part of the course

Building Recommendation Engines with PySpark

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Exercise instructions

  • Create a CrossValidator called cv with our als model as the estimator, setting estimatorParamMaps to the param_grid you just built. Tell Spark that the evaluator to be used is the "evaluator" we built previously. Set the numFolds to 5.
  • Confirm that our cv was built by printing cv.

Hands-on interactive exercise

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

# Build cross validation using CrossValidator
____ = CrossValidator(estimator=____, estimatorParamMaps=____, evaluator=____, numFolds=____)

# Confirm cv was built
print(____)
Edit and Run Code