Make the validator
The submodule pyspark.ml.tuning also has a class called CrossValidator for performing cross validation. This Estimator takes the modeler you want to fit, the grid of hyperparameters you created, and the evaluator you want to use to compare your models.
The submodule pyspark.ml.tune has already been imported as tune. You'll create the CrossValidator by passing it the logistic regression Estimator lr, the parameter grid, and the evaluator you created in the previous exercises.
Это упражнение является частью курса
Foundations of PySpark
Инструкции к упражнению
- Create a
CrossValidatorby callingtune.CrossValidator()with the arguments:estimator=lrestimatorParamMaps=gridevaluator=evaluator
- Name this object
cv.
Интерактивное практическое упражнение
Попробуйте выполнить это упражнение, дополнив этот пример кода.
# Create the CrossValidator
cv = tune.____(estimator=____,
estimatorParamMaps=____,
evaluator=____
)