Packages

class GBTRegressionModel extends RegressionModel[Vector, GBTRegressionModel] with GBTRegressorParams with TreeEnsembleModel[DecisionTreeRegressionModel] with MLWritable with Serializable

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

Annotations
@Since( "1.4.0" )
Source
GBTRegressor.scala
Linear Supertypes
MLWritable, TreeEnsembleModel[DecisionTreeRegressionModel], GBTRegressorParams, TreeRegressorParams, HasVarianceImpurity, TreeEnsembleRegressorParams, GBTParams, HasValidationIndicatorCol, HasStepSize, HasMaxIter, TreeEnsembleParams, DecisionTreeParams, HasWeightCol, HasSeed, HasCheckpointInterval, RegressionModel[Vector, GBTRegressionModel], PredictionModel[Vector, GBTRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Model[GBTRegressionModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. GBTRegressionModel
  2. MLWritable
  3. TreeEnsembleModel
  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. RegressionModel
  18. PredictionModel
  19. PredictorParams
  20. HasPredictionCol
  21. HasFeaturesCol
  22. HasLabelCol
  23. Model
  24. Transformer
  25. PipelineStage
  26. Logging
  27. Params
  28. Serializable
  29. Serializable
  30. Identifiable
  31. AnyRef
  32. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new GBTRegressionModel(uid: String, _trees: Array[DecisionTreeRegressionModel], _treeWeights: Array[Double])

    Construct a GBTRegressionModel

    Construct a GBTRegressionModel

    _trees

    Decision trees in the ensemble.

    _treeWeights

    Weights for the decision trees in the ensemble.

    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[_]): GBTRegressionModel.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): GBTRegressionModel

    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
    GBTRegressionModelModelTransformerPipelineStageParams
    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 evaluateEachIteration(dataset: Dataset[_], loss: String): Array[Double]

    Method to compute error or loss for every iteration of gradient boosting.

    Method to compute error or loss for every iteration of gradient boosting.

    dataset

    Dataset for validation.

    loss

    The loss function used to compute error. Supported options: squared, absolute

    Annotations
    @Since( "2.4.0" )
  16. 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
  17. def explainParams(): String

    Explains all params of this instance.

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

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

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  19. 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
  20. lazy val featureImportances: Vector

    Estimate of the importance of each feature.

    Estimate of the importance of each feature.

    Each feature's importance is the average of its importance across all trees in the ensemble The importance vector is normalized to sum to 1. This method is suggested by Hastie et al. (Hastie, Tibshirani, Friedman. "The Elements of Statistical Learning, 2nd Edition." 2001.) and follows the implementation from scikit-learn.

    Annotations
    @Since( "2.0.0" )
    See also

    DecisionTreeRegressionModel.featureImportances

  21. 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

  22. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  23. def featuresDataType: DataType

    Returns the SQL DataType corresponding to the FeaturesType type parameter.

    Returns the SQL DataType corresponding to the FeaturesType type parameter.

    This is used by validateAndTransformSchema(). This workaround is needed since SQL has different APIs for Scala and Java.

    The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector.

    Attributes
    protected
    Definition Classes
    PredictionModel
  24. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  25. 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
  26. final def getCacheNodeIds: Boolean

    Definition Classes
    DecisionTreeParams
  27. final def getCheckpointInterval: Int

    Definition Classes
    HasCheckpointInterval
  28. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  29. 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
  30. final def getFeatureSubsetStrategy: String

    Definition Classes
    TreeEnsembleParams
  31. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  32. final def getImpurity: String

    Definition Classes
    HasVarianceImpurity
  33. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  34. final def getLeafCol: String

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

    Definition Classes
    GBTRegressorParams
  36. final def getMaxBins: Int

    Definition Classes
    DecisionTreeParams
  37. final def getMaxDepth: Int

    Definition Classes
    DecisionTreeParams
  38. final def getMaxIter: Int

    Definition Classes
    HasMaxIter
  39. final def getMaxMemoryInMB: Int

    Definition Classes
    DecisionTreeParams
  40. final def getMinInfoGain: Double

    Definition Classes
    DecisionTreeParams
  41. final def getMinInstancesPerNode: Int

    Definition Classes
    DecisionTreeParams
  42. final def getMinWeightFractionPerNode: Double

    Definition Classes
    DecisionTreeParams
  43. val getNumTrees: Int

    Number of trees in ensemble

    Number of trees in ensemble

    Annotations
    @Since( "2.0.0" )
  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 hasParent: Boolean

    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  56. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  57. 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
  58. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  59. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  60. 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
  61. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  62. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

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

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  65. 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" )
  66. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  67. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  69. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  72. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  73. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  74. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  77. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  78. 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
  79. 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
  80. 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
  81. final val maxIter: IntParam

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

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

    Definition Classes
    HasMaxIter
  82. 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
  83. 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
  84. 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
  85. 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
  86. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  87. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  88. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  89. val numFeatures: Int

    Returns the number of features the model was trained on.

    Returns the number of features the model was trained on. If unknown, returns -1

    Definition Classes
    GBTRegressionModelPredictionModel
  90. 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.

  91. var parent: Estimator[GBTRegressionModel]

    The parent estimator that produced this model.

    The parent estimator that produced this model.

    Definition Classes
    Model
    Note

    For ensembles' component Models, this value can be null.

  92. def predict(features: Vector): Double

    Predict label for the given features.

    Predict label for the given features. This method is used to implement transform() and output predictionCol.

    Definition Classes
    GBTRegressionModelPredictionModel
  93. def predictLeaf(features: Vector): Vector

    returns

    The indices of the leaves corresponding to the feature vector. Leaves are indexed in pre-order from 0.

    Definition Classes
    TreeEnsembleModel
  94. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  95. 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( ... )
  96. final val seed: LongParam

    Param for random seed.

    Param for random seed.

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

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  98. final def set(param: String, value: Any): GBTRegressionModel.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
  99. final def set[T](param: Param[T], value: T): GBTRegressionModel.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  100. final def setDefault(paramPairs: ParamPair[_]*): GBTRegressionModel.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
  101. final def setDefault[T](param: Param[T], value: T): GBTRegressionModel.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
  102. def setFeaturesCol(value: String): GBTRegressionModel

    Definition Classes
    PredictionModel
  103. final def setLeafCol(value: String): GBTRegressionModel.this.type

    Definition Classes
    DecisionTreeParams
    Annotations
    @Since( "3.0.0" )
  104. def setParent(parent: Estimator[GBTRegressionModel]): GBTRegressionModel

    Sets the parent of this model (Java API).

    Sets the parent of this model (Java API).

    Definition Classes
    Model
  105. def setPredictionCol(value: String): GBTRegressionModel

    Definition Classes
    PredictionModel
  106. 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
  107. 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
  108. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  109. def toDebugString: String

    Full description of model

    Full description of model

    Definition Classes
    TreeEnsembleModel
  110. def toString(): String

    Summary of the model

    Summary of the model

    Definition Classes
    GBTRegressionModel → TreeEnsembleModel → Identifiable → AnyRef → Any
    Annotations
    @Since( "1.4.0" )
  111. lazy val totalNumNodes: Int

    Total number of nodes, summed over all trees in the ensemble.

    Total number of nodes, summed over all trees in the ensemble.

    Definition Classes
    TreeEnsembleModel
  112. def transform(dataset: Dataset[_]): DataFrame

    Transforms dataset by reading from featuresCol, calling predict, and storing the predictions as a new column predictionCol.

    Transforms dataset by reading from featuresCol, calling predict, and storing the predictions as a new column predictionCol.

    dataset

    input dataset

    returns

    transformed dataset with predictionCol of type Double

    Definition Classes
    GBTRegressionModelPredictionModelTransformer
  113. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

    Transforms the dataset with provided parameter map as additional parameters.

    Transforms the dataset with provided parameter map as additional parameters.

    dataset

    input dataset

    paramMap

    additional parameters, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  114. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

    Transforms the dataset with optional parameters

    Transforms the dataset with optional parameters

    dataset

    input dataset

    firstParamPair

    the first param pair, overwrite embedded params

    otherParamPairs

    other param pairs, overwrite embedded params

    returns

    transformed dataset

    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  115. def transformImpl(dataset: Dataset[_]): DataFrame
    Attributes
    protected
    Definition Classes
    PredictionModel
  116. 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
    GBTRegressionModelPredictionModelPipelineStage
    Annotations
    @Since( "1.4.0" )
  117. 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()
  118. def treeWeights: Array[Double]

    Weights for each tree, zippable with trees

    Weights for each tree, zippable with trees

    Definition Classes
    GBTRegressionModel → TreeEnsembleModel
    Annotations
    @Since( "1.4.0" )
  119. def trees: Array[DecisionTreeRegressionModel]

    Trees in this ensemble.

    Trees in this ensemble. Warning: These have null parent Estimators.

    Definition Classes
    GBTRegressionModel → TreeEnsembleModel
    Annotations
    @Since( "1.4.0" )
  120. 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
    GBTRegressionModelIdentifiable
  121. 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
  122. 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
  123. 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

  124. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  125. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  126. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  127. 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
  128. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    GBTRegressionModelMLWritable
    Annotations
    @Since( "2.0.0" )

Inherited from MLWritable

Inherited from TreeEnsembleModel[DecisionTreeRegressionModel]

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 PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Model[GBTRegressionModel]

Inherited from Transformer

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 getters