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.
Bu egzersiz
Foundations of PySpark
kursunun bir parçasıdırEgzersiz talimatları
- Create a
CrossValidatorby callingtune.CrossValidator()with the arguments:estimator=lrestimatorParamMaps=gridevaluator=evaluator
- Name this object
cv.
Uygulamalı interaktif egzersiz
Bu örnek kodu tamamlayarak bu egzersizi bitirin.
# Create the CrossValidator
cv = tune.____(estimator=____,
estimatorParamMaps=____,
evaluator=____
)