Interface GBTParams

All Superinterfaces:
DecisionTreeParams, HasCheckpointInterval, HasFeaturesCol, HasLabelCol, HasMaxIter, HasPredictionCol, HasSeed, HasStepSize, HasValidationIndicatorCol, HasWeightCol, Identifiable, Params, PredictorParams, Serializable, scala.Serializable, TreeEnsembleParams
All Known Subinterfaces:
GBTClassifierParams, GBTRegressorParams
All Known Implementing Classes:
GBTClassificationModel, GBTClassifier, GBTRegressionModel, GBTRegressor

Parameters for Gradient-Boosted Tree algorithms.

Note: Marked as private since this may be made public in the future.

  • Method Details

    • getOldBoostingStrategy

      BoostingStrategy getOldBoostingStrategy(scala.collection.immutable.Map<Object,Object> categoricalFeatures, scala.Enumeration.Value oldAlgo)
      (private[ml]) Create a BoostingStrategy instance to use with the old API.
    • getOldLossType

      Loss getOldLossType()
      Get old Gradient Boosting Loss type
    • getValidationTol

      double getValidationTol()
    • stepSize

      DoubleParam stepSize()
      Param for Step size (a.k.a. learning rate) in interval (0, 1] for shrinking the contribution of each estimator. (default = 0.1)
      Specified by:
      stepSize in interface HasStepSize
    • validationTol

      DoubleParam validationTol()
      Threshold for stopping early when fit with validation is used. (This parameter is ignored when fit without validation is used.) The decision to stop early is decided based on this logic: If the current loss on the validation set is greater than 0.01, the diff of validation error is compared to relative tolerance which is validationTol * (current loss on the validation set). If the current loss on the validation set is less than or equal to 0.01, the diff of validation error is compared to absolute tolerance which is validationTol * 0.01.
      See Also: