Package org.apache.spark.ml.tree
Interface GBTParams
- All Superinterfaces:
DecisionTreeParams
,HasCheckpointInterval
,HasFeaturesCol
,HasLabelCol
,HasMaxIter
,HasPredictionCol
,HasSeed
,HasStepSize
,HasValidationIndicatorCol
,HasWeightCol
,Identifiable
,Params
,PredictorParams
,Serializable
,TreeEnsembleParams
- All Known Subinterfaces:
GBTClassifierParams
,GBTRegressorParams
- All Known Implementing Classes:
GBTClassificationModel
,GBTClassifier
,GBTRegressionModel
,GBTRegressor
public interface GBTParams
extends TreeEnsembleParams, HasMaxIter, HasStepSize, HasValidationIndicatorCol
Parameters for Gradient-Boosted Tree algorithms.
Note: Marked as private since this may be made public in the future.
-
Method Summary
Modifier and TypeMethodDescriptiongetOldBoostingStrategy
(scala.collection.immutable.Map<Object, Object> categoricalFeatures, scala.Enumeration.Value oldAlgo) (private[ml]) Create a BoostingStrategy instance to use with the old API.Get old Gradient Boosting Loss typedouble
stepSize()
Param for Step size (a.k.a.Threshold for stopping early when fit with validation is used.Methods inherited from interface org.apache.spark.ml.tree.DecisionTreeParams
cacheNodeIds, getCacheNodeIds, getLeafCol, getMaxBins, getMaxDepth, getMaxMemoryInMB, getMinInfoGain, getMinInstancesPerNode, getMinWeightFractionPerNode, getOldStrategy, leafCol, maxBins, maxDepth, maxMemoryInMB, minInfoGain, minInstancesPerNode, minWeightFractionPerNode, setLeafCol
Methods inherited from interface org.apache.spark.ml.param.shared.HasCheckpointInterval
checkpointInterval, getCheckpointInterval
Methods inherited from interface org.apache.spark.ml.param.shared.HasFeaturesCol
featuresCol, getFeaturesCol
Methods inherited from interface org.apache.spark.ml.param.shared.HasLabelCol
getLabelCol, labelCol
Methods inherited from interface org.apache.spark.ml.param.shared.HasMaxIter
getMaxIter, maxIter
Methods inherited from interface org.apache.spark.ml.param.shared.HasPredictionCol
getPredictionCol, predictionCol
Methods inherited from interface org.apache.spark.ml.param.shared.HasStepSize
getStepSize
Methods inherited from interface org.apache.spark.ml.param.shared.HasValidationIndicatorCol
getValidationIndicatorCol, validationIndicatorCol
Methods inherited from interface org.apache.spark.ml.param.shared.HasWeightCol
getWeightCol, weightCol
Methods inherited from interface org.apache.spark.ml.util.Identifiable
toString, uid
Methods inherited from interface org.apache.spark.ml.param.Params
clear, copy, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn
Methods inherited from interface org.apache.spark.ml.PredictorParams
validateAndTransformSchema
Methods inherited from interface org.apache.spark.ml.tree.TreeEnsembleParams
featureSubsetStrategy, getFeatureSubsetStrategy, getOldStrategy, getSubsamplingRate, subsamplingRate
-
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 interfaceHasStepSize
- Returns:
- (undocumented)
-
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.- Returns:
- (undocumented)
- See Also:
-