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.
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Method SummaryModifier 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 typedoublestepSize()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.DecisionTreeParamscacheNodeIds, getCacheNodeIds, getLeafCol, getMaxBins, getMaxDepth, getMaxMemoryInMB, getMinInfoGain, getMinInstancesPerNode, getMinWeightFractionPerNode, getOldStrategy, leafCol, maxBins, maxDepth, maxMemoryInMB, minInfoGain, minInstancesPerNode, minWeightFractionPerNode, setLeafColMethods inherited from interface org.apache.spark.ml.param.shared.HasCheckpointIntervalcheckpointInterval, getCheckpointIntervalMethods inherited from interface org.apache.spark.ml.param.shared.HasFeaturesColfeaturesCol, getFeaturesColMethods inherited from interface org.apache.spark.ml.param.shared.HasLabelColgetLabelCol, labelColMethods inherited from interface org.apache.spark.ml.param.shared.HasMaxItergetMaxIter, maxIterMethods inherited from interface org.apache.spark.ml.param.shared.HasPredictionColgetPredictionCol, predictionColMethods inherited from interface org.apache.spark.ml.param.shared.HasStepSizegetStepSizeMethods inherited from interface org.apache.spark.ml.param.shared.HasValidationIndicatorColgetValidationIndicatorCol, validationIndicatorColMethods inherited from interface org.apache.spark.ml.param.shared.HasWeightColgetWeightCol, weightColMethods inherited from interface org.apache.spark.ml.util.IdentifiabletoString, uidMethods inherited from interface org.apache.spark.ml.param.Paramsclear, copy, copyValues, defaultCopy, defaultParamMap, estimateMatadataSize, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwnMethods inherited from interface org.apache.spark.ml.PredictorParamsvalidateAndTransformSchemaMethods inherited from interface org.apache.spark.ml.tree.TreeEnsembleParamsfeatureSubsetStrategy, getFeatureSubsetStrategy, getOldStrategy, getSubsamplingRate, subsamplingRate
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Method Details- 
getOldBoostingStrategyBoostingStrategy getOldBoostingStrategy(scala.collection.immutable.Map<Object, Object> categoricalFeatures, scala.Enumeration.Value oldAlgo) (private[ml]) Create a BoostingStrategy instance to use with the old API.
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getOldLossTypeLoss getOldLossType()Get old Gradient Boosting Loss type
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getValidationToldouble getValidationTol()
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stepSizeDoubleParam 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:
- stepSizein interface- HasStepSize
- Returns:
- (undocumented)
 
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validationTolDoubleParam 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:
 
 
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