org.apache.spark.ml.tree

Interface GBTParams

All Superinterfaces:

DecisionTreeParams, HasCheckpointInterval, HasFeaturesCol, HasLabelCol, HasMaxIter, HasPredictionCol, HasSeed, HasStepSize, HasValidationIndicatorCol, HasWeightCol, Identifiable, Params, PredictorParams, java.io.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 and DeveloperApi since this may be made public in the future.

Method Summary

Modifier and Type	Method and Description
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.
Loss	getOldLossType() Get old Gradient Boosting Loss type
double	getValidationTol()
DoubleParam	stepSize() Param for Step size (a.k.a.
DoubleParam	validationTol() Threshold for stopping early when fit with validation is used.

Methods inherited from interface org.apache.spark.ml.tree.TreeEnsembleParams

featureSubsetStrategy, getFeatureSubsetStrategy, getOldStrategy, getSubsamplingRate, subsamplingRate

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.PredictorParams

extractInstances, extractInstances, validateAndTransformSchema

Methods inherited from interface org.apache.spark.ml.param.shared.HasLabelCol

getLabelCol, labelCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasFeaturesCol

featuresCol, getFeaturesCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasPredictionCol

getPredictionCol, predictionCol

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, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn

Methods inherited from interface org.apache.spark.ml.util.Identifiable

toString, uid

Methods inherited from interface org.apache.spark.ml.param.shared.HasCheckpointInterval

checkpointInterval, getCheckpointInterval

Methods inherited from interface org.apache.spark.ml.param.shared.HasSeed

getSeed, seed

Methods inherited from interface org.apache.spark.ml.param.shared.HasWeightCol

getWeightCol, weightCol

Methods inherited from interface org.apache.spark.ml.param.shared.HasMaxIter

getMaxIter, maxIter

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

Method Detail

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

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:

validationIndicatorCol