Packages

class GBTRegressor extends Regressor[Vector, GBTRegressor, GBTRegressionModel] with GBTRegressorParams with DefaultParamsWritable with Logging

Gradient-Boosted Trees (GBTs) learning algorithm for regression. It supports both continuous and categorical features.

The implementation is based upon: J.H. Friedman. "Stochastic Gradient Boosting." 1999.

Notes on Gradient Boosting vs. TreeBoost:

  • This implementation is for Stochastic Gradient Boosting, not for TreeBoost.
  • Both algorithms learn tree ensembles by minimizing loss functions.
  • TreeBoost (Friedman, 1999) additionally modifies the outputs at tree leaf nodes based on the loss function, whereas the original gradient boosting method does not.
    • When the loss is SquaredError, these methods give the same result, but they could differ for other loss functions.
  • We expect to implement TreeBoost in the future: [https://issues.apache.org/jira/browse/SPARK-4240]
Annotations
@Since( "1.4.0" )
Source
GBTRegressor.scala
Linear Supertypes
DefaultParamsWritable, MLWritable, GBTRegressorParams, TreeRegressorParams, HasVarianceImpurity, TreeEnsembleRegressorParams, GBTParams, HasValidationIndicatorCol, HasStepSize, HasMaxIter, TreeEnsembleParams, DecisionTreeParams, HasWeightCol, HasSeed, HasCheckpointInterval, Regressor[Vector, GBTRegressor, GBTRegressionModel], Predictor[Vector, GBTRegressor, GBTRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Estimator[GBTRegressionModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. GBTRegressor
  2. DefaultParamsWritable
  3. MLWritable
  4. GBTRegressorParams
  5. TreeRegressorParams
  6. HasVarianceImpurity
  7. TreeEnsembleRegressorParams
  8. GBTParams
  9. HasValidationIndicatorCol
  10. HasStepSize
  11. HasMaxIter
  12. TreeEnsembleParams
  13. DecisionTreeParams
  14. HasWeightCol
  15. HasSeed
  16. HasCheckpointInterval
  17. Regressor
  18. Predictor
  19. PredictorParams
  20. HasPredictionCol
  21. HasFeaturesCol
  22. HasLabelCol
  23. Estimator
  24. PipelineStage
  25. Logging
  26. Params
  27. Serializable
  28. Serializable
  29. Identifiable
  30. AnyRef
  31. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new GBTRegressor()
    Annotations
    @Since( "1.4.0" )
  2. new GBTRegressor(uid: String)
    Annotations
    @Since( "1.4.0" )

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. final val cacheNodeIds: BooleanParam

    If false, the algorithm will pass trees to executors to match instances with nodes.

    If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees. Users can set how often should the cache be checkpointed or disable it by setting checkpointInterval. (default = false)

    Definition Classes
    DecisionTreeParams
  7. final val checkpointInterval: IntParam

    Param for set checkpoint interval (>= 1) or disable checkpoint (-1).

    Param for set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations. Note: this setting will be ignored if the checkpoint directory is not set in the SparkContext.

    Definition Classes
    HasCheckpointInterval
  8. final def clear(param: Param[_]): GBTRegressor.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  9. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  10. def copy(extra: ParamMap): GBTRegressor

    Creates a copy of this instance with the same UID and some extra params.

    Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

    Definition Classes
    GBTRegressor → Predictor → Estimator → PipelineStage → Params
    Annotations
    @Since( "1.4.0" )
  11. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

    Copies param values from this instance to another instance for params shared by them.

    Copies param values from this instance to another instance for params shared by them.

    This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.

    to

    the target instance, which should work with the same set of default Params as this source instance

    extra

    extra params to be copied to the target's paramMap

    returns

    the target instance with param values copied

    Attributes
    protected
    Definition Classes
    Params
  12. final def defaultCopy[T <: Params](extra: ParamMap): T

    Default implementation of copy with extra params.

    Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

    Attributes
    protected
    Definition Classes
    Params
  13. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  14. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  15. def explainParam(param: Param[_]): String

    Explains a param.

    Explains a param.

    param

    input param, must belong to this instance.

    returns

    a string that contains the input param name, doc, and optionally its default value and the user-supplied value

    Definition Classes
    Params
  16. def explainParams(): String

    Explains all params of this instance.

    Explains all params of this instance. See explainParam().

    Definition Classes
    Params
  17. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  18. final def extractParamMap(extra: ParamMap): ParamMap

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Definition Classes
    Params
  19. final val featureSubsetStrategy: Param[String]

    The number of features to consider for splits at each tree node.

    The number of features to consider for splits at each tree node. Supported options:

    • "auto": Choose automatically for task: If numTrees == 1, set to "all." If numTrees greater than 1 (forest), set to "sqrt" for classification and to "onethird" for regression.
    • "all": use all features
    • "onethird": use 1/3 of the features
    • "sqrt": use sqrt(number of features)
    • "log2": use log2(number of features)
    • "n": when n is in the range (0, 1.0], use n * number of features. When n is in the range (1, number of features), use n features. (default = "auto")

    These various settings are based on the following references:

    • log2: tested in Breiman (2001)
    • sqrt: recommended by Breiman manual for random forests
    • The defaults of sqrt (classification) and onethird (regression) match the R randomForest package.
    Definition Classes
    TreeEnsembleParams
    See also

    Breiman (2001)

    Breiman manual for random forests

  20. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  21. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  22. def fit(dataset: Dataset[_]): GBTRegressionModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes
    Predictor → Estimator
  23. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[GBTRegressionModel]

    Fits multiple models to the input data with multiple sets of parameters.

    Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could override this to optimize multi-model training.

    dataset

    input dataset

    paramMaps

    An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted models, matching the input parameter maps

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  24. def fit(dataset: Dataset[_], paramMap: ParamMap): GBTRegressionModel

    Fits a single model to the input data with provided parameter map.

    Fits a single model to the input data with provided parameter map.

    dataset

    input dataset

    paramMap

    Parameter map. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  25. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): GBTRegressionModel

    Fits a single model to the input data with optional parameters.

    Fits a single model to the input data with optional parameters.

    dataset

    input dataset

    firstParamPair

    the first param pair, overrides embedded params

    otherParamPairs

    other param pairs. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  26. final def get[T](param: Param[T]): Option[T]

    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes
    Params
  27. final def getCacheNodeIds: Boolean

    Definition Classes
    DecisionTreeParams
  28. final def getCheckpointInterval: Int

    Definition Classes
    HasCheckpointInterval
  29. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  30. final def getDefault[T](param: Param[T]): Option[T]

    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  31. final def getFeatureSubsetStrategy: String

    Definition Classes
    TreeEnsembleParams
  32. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  33. final def getImpurity: String

    Definition Classes
    HasVarianceImpurity
  34. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  35. final def getLeafCol: String

    Definition Classes
    DecisionTreeParams
    Annotations
    @Since( "3.0.0" )
  36. def getLossType: String

    Definition Classes
    GBTRegressorParams
  37. final def getMaxBins: Int

    Definition Classes
    DecisionTreeParams
  38. final def getMaxDepth: Int

    Definition Classes
    DecisionTreeParams
  39. final def getMaxIter: Int

    Definition Classes
    HasMaxIter
  40. final def getMaxMemoryInMB: Int

    Definition Classes
    DecisionTreeParams
  41. final def getMinInfoGain: Double

    Definition Classes
    DecisionTreeParams
  42. final def getMinInstancesPerNode: Int

    Definition Classes
    DecisionTreeParams
  43. final def getMinWeightFractionPerNode: Double

    Definition Classes
    DecisionTreeParams
  44. final def getOrDefault[T](param: Param[T]): T

    Gets the value of a param in the embedded param map or its default value.

    Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

    Definition Classes
    Params
  45. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  46. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  47. final def getSeed: Long

    Definition Classes
    HasSeed
  48. final def getStepSize: Double

    Definition Classes
    HasStepSize
  49. final def getSubsamplingRate: Double

    Definition Classes
    TreeEnsembleParams
  50. final def getValidationIndicatorCol: String

    Definition Classes
    HasValidationIndicatorCol
  51. final def getValidationTol: Double

    Definition Classes
    GBTParams
    Annotations
    @Since( "2.4.0" )
  52. final def getWeightCol: String

    Definition Classes
    HasWeightCol
  53. final def hasDefault[T](param: Param[T]): Boolean

    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

    Definition Classes
    Params
  54. def hasParam(paramName: String): Boolean

    Tests whether this instance contains a param with a given name.

    Tests whether this instance contains a param with a given name.

    Definition Classes
    Params
  55. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  56. final val impurity: Param[String]

    Criterion used for information gain calculation (case-insensitive).

    Criterion used for information gain calculation (case-insensitive). This impurity type is used in DecisionTreeRegressor, RandomForestRegressor, GBTRegressor and GBTClassifier (since GBTClassificationModel is internally composed of DecisionTreeRegressionModels). Supported: "variance". (default = variance)

    Definition Classes
    HasVarianceImpurity
  57. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  58. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  59. final def isDefined(param: Param[_]): Boolean

    Checks whether a param is explicitly set or has a default value.

    Checks whether a param is explicitly set or has a default value.

    Definition Classes
    Params
  60. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  61. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  62. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  63. final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  64. final val leafCol: Param[String]

    Leaf indices column name.

    Leaf indices column name. Predicted leaf index of each instance in each tree by preorder. (default = "")

    Definition Classes
    DecisionTreeParams
    Annotations
    @Since( "3.0.0" )
  65. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  66. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  67. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  69. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  72. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  73. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  77. val lossType: Param[String]

    Loss function which GBT tries to minimize.

    Loss function which GBT tries to minimize. (case-insensitive) Supported: "squared" (L2) and "absolute" (L1) (default = squared)

    Definition Classes
    GBTRegressorParams
  78. final val maxBins: IntParam

    Maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node.

    Maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node. More bins give higher granularity. Must be at least 2 and at least number of categories in any categorical feature. (default = 32)

    Definition Classes
    DecisionTreeParams
  79. final val maxDepth: IntParam

    Maximum depth of the tree (nonnegative).

    Maximum depth of the tree (nonnegative). E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. (default = 5)

    Definition Classes
    DecisionTreeParams
  80. final val maxIter: IntParam

    Param for maximum number of iterations (>= 0).

    Param for maximum number of iterations (>= 0).

    Definition Classes
    HasMaxIter
  81. final val maxMemoryInMB: IntParam

    Maximum memory in MB allocated to histogram aggregation.

    Maximum memory in MB allocated to histogram aggregation. If too small, then 1 node will be split per iteration, and its aggregates may exceed this size. (default = 256 MB)

    Definition Classes
    DecisionTreeParams
  82. final val minInfoGain: DoubleParam

    Minimum information gain for a split to be considered at a tree node.

    Minimum information gain for a split to be considered at a tree node. Should be at least 0.0. (default = 0.0)

    Definition Classes
    DecisionTreeParams
  83. final val minInstancesPerNode: IntParam

    Minimum number of instances each child must have after split.

    Minimum number of instances each child must have after split. If a split causes the left or right child to have fewer than minInstancesPerNode, the split will be discarded as invalid. Must be at least 1. (default = 1)

    Definition Classes
    DecisionTreeParams
  84. final val minWeightFractionPerNode: DoubleParam

    Minimum fraction of the weighted sample count that each child must have after split.

    Minimum fraction of the weighted sample count that each child must have after split. If a split causes the fraction of the total weight in the left or right child to be less than minWeightFractionPerNode, the split will be discarded as invalid. Should be in the interval [0.0, 0.5). (default = 0.0)

    Definition Classes
    DecisionTreeParams
  85. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  86. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  87. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  88. lazy val params: Array[Param[_]]

    Returns all params sorted by their names.

    Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.

    Definition Classes
    Params
    Note

    Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

  89. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  90. def save(path: String): Unit

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  91. final val seed: LongParam

    Param for random seed.

    Param for random seed.

    Definition Classes
    HasSeed
  92. final def set(paramPair: ParamPair[_]): GBTRegressor.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  93. final def set(param: String, value: Any): GBTRegressor.this.type

    Sets a parameter (by name) in the embedded param map.

    Sets a parameter (by name) in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  94. final def set[T](param: Param[T], value: T): GBTRegressor.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  95. def setCacheNodeIds(value: Boolean): GBTRegressor.this.type

    Annotations
    @Since( "1.4.0" )
  96. def setCheckpointInterval(value: Int): GBTRegressor.this.type

    Specifies how often to checkpoint the cached node IDs.

    Specifies how often to checkpoint the cached node IDs. E.g. 10 means that the cache will get checkpointed every 10 iterations. This is only used if cacheNodeIds is true and if the checkpoint directory is set in org.apache.spark.SparkContext. Must be at least 1. (default = 10)

    Annotations
    @Since( "1.4.0" )
  97. final def setDefault(paramPairs: ParamPair[_]*): GBTRegressor.this.type

    Sets default values for a list of params.

    Sets default values for a list of params.

    Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.

    paramPairs

    a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

    Attributes
    protected
    Definition Classes
    Params
  98. final def setDefault[T](param: Param[T], value: T): GBTRegressor.this.type

    Sets a default value for a param.

    Sets a default value for a param.

    param

    param to set the default value. Make sure that this param is initialized before this method gets called.

    value

    the default value

    Attributes
    protected[ml]
    Definition Classes
    Params
  99. def setFeatureSubsetStrategy(value: String): GBTRegressor.this.type

    Annotations
    @Since( "2.3.0" )
  100. def setFeaturesCol(value: String): GBTRegressor

    Definition Classes
    Predictor
  101. def setImpurity(value: String): GBTRegressor.this.type

    The impurity setting is ignored for GBT models.

    The impurity setting is ignored for GBT models. Individual trees are built using impurity "Variance."

    Annotations
    @Since( "1.4.0" )
  102. def setLabelCol(value: String): GBTRegressor

    Definition Classes
    Predictor
  103. final def setLeafCol(value: String): GBTRegressor.this.type

    Definition Classes
    DecisionTreeParams
    Annotations
    @Since( "3.0.0" )
  104. def setLossType(value: String): GBTRegressor.this.type

    Annotations
    @Since( "1.4.0" )
  105. def setMaxBins(value: Int): GBTRegressor.this.type

    Annotations
    @Since( "1.4.0" )
  106. def setMaxDepth(value: Int): GBTRegressor.this.type

    Annotations
    @Since( "1.4.0" )
  107. def setMaxIter(value: Int): GBTRegressor.this.type

    Annotations
    @Since( "1.4.0" )
  108. def setMaxMemoryInMB(value: Int): GBTRegressor.this.type

    Annotations
    @Since( "1.4.0" )
  109. def setMinInfoGain(value: Double): GBTRegressor.this.type

    Annotations
    @Since( "1.4.0" )
  110. def setMinInstancesPerNode(value: Int): GBTRegressor.this.type

    Annotations
    @Since( "1.4.0" )
  111. def setMinWeightFractionPerNode(value: Double): GBTRegressor.this.type

    Annotations
    @Since( "3.0.0" )
  112. def setPredictionCol(value: String): GBTRegressor

    Definition Classes
    Predictor
  113. def setSeed(value: Long): GBTRegressor.this.type

    Annotations
    @Since( "1.4.0" )
  114. def setStepSize(value: Double): GBTRegressor.this.type

    Annotations
    @Since( "1.4.0" )
  115. def setSubsamplingRate(value: Double): GBTRegressor.this.type

    Annotations
    @Since( "1.4.0" )
  116. def setValidationIndicatorCol(value: String): GBTRegressor.this.type

    Annotations
    @Since( "2.4.0" )
  117. def setWeightCol(value: String): GBTRegressor.this.type

    Sets the value of param weightCol.

    Sets the value of param weightCol. If this is not set or empty, we treat all instance weights as 1.0. By default the weightCol is not set, so all instances have weight 1.0.

    Annotations
    @Since( "3.0.0" )
  118. final val stepSize: DoubleParam

    Param for Step size (a.k.a.

    Param for Step size (a.k.a. learning rate) in interval (0, 1] for shrinking the contribution of each estimator. (default = 0.1)

    Definition Classes
    GBTParams → HasStepSize
  119. final val subsamplingRate: DoubleParam

    Fraction of the training data used for learning each decision tree, in range (0, 1].

    Fraction of the training data used for learning each decision tree, in range (0, 1]. (default = 1.0)

    Definition Classes
    TreeEnsembleParams
  120. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  121. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  122. def train(dataset: Dataset[_]): GBTRegressionModel

    Train a model using the given dataset and parameters.

    Train a model using the given dataset and parameters. Developers can implement this instead of fit() to avoid dealing with schema validation and copying parameters into the model.

    dataset

    Training dataset

    returns

    Fitted model

    Attributes
    protected
    Definition Classes
    GBTRegressor → Predictor
  123. def transformSchema(schema: StructType): StructType

    Check transform validity and derive the output schema from the input schema.

    Check transform validity and derive the output schema from the input schema.

    We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

    Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

    Definition Classes
    Predictor → PipelineStage
  124. def transformSchema(schema: StructType, logging: Boolean): StructType

    :: DeveloperApi ::

    :: DeveloperApi ::

    Derives the output schema from the input schema and parameters, optionally with logging.

    This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.

    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  125. val uid: String

    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    GBTRegressor → Identifiable
    Annotations
    @Since( "1.4.0" )
  126. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

    Validates and transforms the input schema with the provided param map.

    Validates and transforms the input schema with the provided param map.

    schema

    input schema

    fitting

    whether this is in fitting

    featuresDataType

    SQL DataType for FeaturesType. E.g., VectorUDT for vector features.

    returns

    output schema

    Attributes
    protected
    Definition Classes
    TreeEnsembleRegressorParams → PredictorParams
  127. final val validationIndicatorCol: Param[String]

    Param for name of the column that indicates whether each row is for training or for validation.

    Param for name of the column that indicates whether each row is for training or for validation. False indicates training; true indicates validation..

    Definition Classes
    HasValidationIndicatorCol
  128. final val validationTol: DoubleParam

    Threshold for stopping early when fit with validation is used.

    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.

    Definition Classes
    GBTParams
    Annotations
    @Since( "2.4.0" )
    See also

    validationIndicatorCol

  129. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  130. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  131. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  132. final val weightCol: Param[String]

    Param for weight column name.

    Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.

    Definition Classes
    HasWeightCol
  133. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from GBTRegressorParams

Inherited from TreeRegressorParams

Inherited from HasVarianceImpurity

Inherited from TreeEnsembleRegressorParams

Inherited from GBTParams

Inherited from HasValidationIndicatorCol

Inherited from HasStepSize

Inherited from HasMaxIter

Inherited from TreeEnsembleParams

Inherited from DecisionTreeParams

Inherited from HasWeightCol

Inherited from HasSeed

Inherited from HasCheckpointInterval

Inherited from Regressor[Vector, GBTRegressor, GBTRegressionModel]

Inherited from Predictor[Vector, GBTRegressor, GBTRegressionModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Estimator[GBTRegressionModel]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

Members

Parameter setters

Parameter getters

(expert-only) Parameters

A list of advanced, expert-only (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

(expert-only) Parameter setters

(expert-only) Parameter getters