Class/Object

org.apache.spark.ml.classification

GBTClassificationModel

Related Docs: object GBTClassificationModel | package classification

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class GBTClassificationModel extends ProbabilisticClassificationModel[Vector, GBTClassificationModel] with GBTClassifierParams with TreeEnsembleModel[DecisionTreeRegressionModel] with MLWritable with Serializable

Gradient-Boosted Trees (GBTs) (http://en.wikipedia.org/wiki/Gradient_boosting) model for classification. It supports binary labels, as well as both continuous and categorical features.

Annotations
@Since( "1.6.0" )
Source
GBTClassifier.scala
Note

Multiclass labels are not currently supported.

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Inherited
  1. GBTClassificationModel
  2. MLWritable
  3. TreeEnsembleModel
  4. GBTClassifierParams
  5. TreeClassifierParams
  6. GBTParams
  7. HasValidationIndicatorCol
  8. HasStepSize
  9. HasMaxIter
  10. TreeEnsembleParams
  11. DecisionTreeParams
  12. HasSeed
  13. HasCheckpointInterval
  14. ProbabilisticClassificationModel
  15. ProbabilisticClassifierParams
  16. HasThresholds
  17. HasProbabilityCol
  18. ClassificationModel
  19. ClassifierParams
  20. HasRawPredictionCol
  21. PredictionModel
  22. PredictorParams
  23. HasPredictionCol
  24. HasFeaturesCol
  25. HasLabelCol
  26. Model
  27. Transformer
  28. PipelineStage
  29. Logging
  30. Params
  31. Serializable
  32. Serializable
  33. Identifiable
  34. AnyRef
  35. Any
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Visibility
  1. Public
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Instance Constructors

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

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    Construct a GBTClassificationModel

    Construct a GBTClassificationModel

    _trees

    Decision trees in the ensemble.

    _treeWeights

    Weights for the decision trees in the ensemble.

    Annotations
    @Since( "1.6.0" )

Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

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    An alias for getOrDefault().

    An alias for getOrDefault().

    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  5. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  6. final val cacheNodeIds: BooleanParam

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    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

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    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[_]): GBTClassificationModel.this.type

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    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

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  10. def copy(extra: ParamMap): GBTClassificationModel

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    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
    GBTClassificationModelModelTransformerPipelineStageParams
    Annotations
    @Since( "1.4.0" )
  11. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

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    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

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    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

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    Definition Classes
    AnyRef
  14. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  15. def evaluateEachIteration(dataset: Dataset[_]): Array[Double]

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    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.

    Annotations
    @Since( "2.4.0" )
  16. def explainParam(param: Param[_]): String

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    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

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    Explains all params of this instance.

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

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

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    extractParamMap with no extra values.

    extractParamMap with no extra values.

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

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    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

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    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.

    See DecisionTreeClassificationModel.featureImportances

    Annotations
    @Since( "2.0.0" )
  21. final val featureSubsetStrategy: Param[String]

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    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 > 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 manual for random forests

    Breiman (2001)

  22. final val featuresCol: Param[String]

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    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  23. def featuresDataType: DataType

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    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

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  25. final def get[T](param: Param[T]): Option[T]

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    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

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    Definition Classes
    DecisionTreeParams
  27. final def getCheckpointInterval: Int

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    Definition Classes
    HasCheckpointInterval
  28. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  29. final def getDefault[T](param: Param[T]): Option[T]

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    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  30. final def getFeatureSubsetStrategy: String

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    Definition Classes
    TreeEnsembleParams
  31. final def getFeaturesCol: String

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    Definition Classes
    HasFeaturesCol
  32. final def getImpurity: String

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    Definition Classes
    TreeClassifierParams
  33. final def getLabelCol: String

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    Definition Classes
    HasLabelCol
  34. def getLossType: String

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    Definition Classes
    GBTClassifierParams
  35. final def getMaxBins: Int

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    Definition Classes
    DecisionTreeParams
  36. final def getMaxDepth: Int

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    Definition Classes
    DecisionTreeParams
  37. final def getMaxIter: Int

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    Definition Classes
    HasMaxIter
  38. final def getMaxMemoryInMB: Int

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    Definition Classes
    DecisionTreeParams
  39. final def getMinInfoGain: Double

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    Definition Classes
    DecisionTreeParams
  40. final def getMinInstancesPerNode: Int

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    Definition Classes
    DecisionTreeParams
  41. val getNumTrees: Int

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    Number of trees in ensemble

    Number of trees in ensemble

    Annotations
    @Since( "2.0.0" )
  42. final def getOrDefault[T](param: Param[T]): T

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    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
  43. def getParam(paramName: String): Param[Any]

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    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  44. final def getPredictionCol: String

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    Definition Classes
    HasPredictionCol
  45. final def getProbabilityCol: String

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    Definition Classes
    HasProbabilityCol
  46. final def getRawPredictionCol: String

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    Definition Classes
    HasRawPredictionCol
  47. final def getSeed: Long

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    Definition Classes
    HasSeed
  48. final def getStepSize: Double

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    Definition Classes
    HasStepSize
  49. final def getSubsamplingRate: Double

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    Definition Classes
    TreeEnsembleParams
  50. def getThresholds: Array[Double]

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    Definition Classes
    HasThresholds
  51. final def getValidationIndicatorCol: String

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    Definition Classes
    HasValidationIndicatorCol
  52. final def getValidationTol: Double

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    Definition Classes
    GBTParams
    Annotations
    @Since( "2.4.0" )
  53. final def hasDefault[T](param: Param[T]): Boolean

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    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

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    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

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    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes
    Model
  56. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  57. final val impurity: Param[String]

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

    Criterion used for information gain calculation (case-insensitive). Supported: "entropy" and "gini". (default = gini)

    Definition Classes
    TreeClassifierParams
  58. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  59. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  60. final def isDefined(param: Param[_]): Boolean

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    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

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    Definition Classes
    Any
  62. final def isSet(param: Param[_]): Boolean

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    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  63. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  64. final val labelCol: Param[String]

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    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  65. def log: Logger

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    Attributes
    protected
    Definition Classes
    Logging
  66. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  67. def logDebug(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  68. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  69. def logError(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  70. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  71. def logInfo(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  72. def logName: String

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    Attributes
    protected
    Definition Classes
    Logging
  73. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  74. def logTrace(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  75. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  76. def logWarning(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  77. val lossType: Param[String]

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    Loss function which GBT tries to minimize.

    Loss function which GBT tries to minimize. (case-insensitive) Supported: "logistic" (default = logistic)

    Definition Classes
    GBTClassifierParams
  78. final val maxBins: IntParam

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    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 >= 2 and >= number of categories in any categorical feature. (default = 32)

    Definition Classes
    DecisionTreeParams
  79. final val maxDepth: IntParam

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    Maximum depth of the tree (>= 0).

    Maximum depth of the tree (>= 0). 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

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

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

    Definition Classes
    HasMaxIter
  81. final val maxMemoryInMB: IntParam

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    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

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    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 >= 0.0. (default = 0.0)

    Definition Classes
    DecisionTreeParams
  83. final val minInstancesPerNode: IntParam

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    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. Should be >= 1. (default = 1)

    Definition Classes
    DecisionTreeParams
  84. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  85. final def notify(): Unit

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    Definition Classes
    AnyRef
  86. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  87. val numClasses: Int

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    Number of classes (values which the label can take).

    Number of classes (values which the label can take).

    Definition Classes
    GBTClassificationModelClassificationModel
    Annotations
    @Since( "2.2.0" )
  88. val numFeatures: Int

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    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
    GBTClassificationModelPredictionModel
    Annotations
    @Since( "1.6.0" )
  89. val numTrees: Int

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    Number of trees in ensemble

  90. lazy val params: Array[Param[_]]

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    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[GBTClassificationModel]

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    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

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    Predict label for the given features.

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

    This default implementation for classification predicts the index of the maximum value from predictRaw().

    Definition Classes
    GBTClassificationModelClassificationModelPredictionModel
  93. def predictProbability(features: Vector): Vector

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    Predict the probability of each class given the features.

    Predict the probability of each class given the features. These predictions are also called class conditional probabilities.

    This internal method is used to implement transform() and output probabilityCol.

    returns

    Estimated class conditional probabilities

    Attributes
    protected
    Definition Classes
    ProbabilisticClassificationModel
  94. def predictRaw(features: Vector): Vector

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    Raw prediction for each possible label.

    Raw prediction for each possible label. The meaning of a "raw" prediction may vary between algorithms, but it intuitively gives a measure of confidence in each possible label (where larger = more confident). This internal method is used to implement transform() and output rawPredictionCol.

    returns

    vector where element i is the raw prediction for label i. This raw prediction may be any real number, where a larger value indicates greater confidence for that label.

    Attributes
    protected
    Definition Classes
    GBTClassificationModelClassificationModel
  95. final val predictionCol: Param[String]

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    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  96. def probability2prediction(probability: Vector): Double

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    Given a vector of class conditional probabilities, select the predicted label.

    Given a vector of class conditional probabilities, select the predicted label. This supports thresholds which favor particular labels.

    returns

    predicted label

    Attributes
    protected
    Definition Classes
    ProbabilisticClassificationModel
  97. final val probabilityCol: Param[String]

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    Param for Column name for predicted class conditional probabilities.

    Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.

    Definition Classes
    HasProbabilityCol
  98. def raw2prediction(rawPrediction: Vector): Double

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    Given a vector of raw predictions, select the predicted label.

    Given a vector of raw predictions, select the predicted label. This may be overridden to support thresholds which favor particular labels.

    returns

    predicted label

    Attributes
    protected
    Definition Classes
    ProbabilisticClassificationModelClassificationModel
  99. def raw2probability(rawPrediction: Vector): Vector

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    Non-in-place version of raw2probabilityInPlace()

    Non-in-place version of raw2probabilityInPlace()

    Attributes
    protected
    Definition Classes
    ProbabilisticClassificationModel
  100. def raw2probabilityInPlace(rawPrediction: Vector): Vector

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    Estimate the probability of each class given the raw prediction, doing the computation in-place.

    Estimate the probability of each class given the raw prediction, doing the computation in-place. These predictions are also called class conditional probabilities.

    This internal method is used to implement transform() and output probabilityCol.

    returns

    Estimated class conditional probabilities (modified input vector)

    Attributes
    protected
    Definition Classes
    GBTClassificationModelProbabilisticClassificationModel
  101. final val rawPredictionCol: Param[String]

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    Param for raw prediction (a.k.a.

    Param for raw prediction (a.k.a. confidence) column name.

    Definition Classes
    HasRawPredictionCol
  102. def save(path: String): Unit

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    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( ... )
  103. final val seed: LongParam

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    Param for random seed.

    Param for random seed.

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

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    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

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

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    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
  106. final def set[T](param: Param[T], value: T): GBTClassificationModel.this.type

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    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  107. final def setDefault(paramPairs: ParamPair[_]*): GBTClassificationModel.this.type

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    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
  108. final def setDefault[T](param: Param[T], value: T): GBTClassificationModel.this.type

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    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
    Definition Classes
    Params
  109. def setFeaturesCol(value: String): GBTClassificationModel

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    Definition Classes
    PredictionModel
  110. def setParent(parent: Estimator[GBTClassificationModel]): GBTClassificationModel

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    Sets the parent of this model (Java API).

    Sets the parent of this model (Java API).

    Definition Classes
    Model
  111. def setPredictionCol(value: String): GBTClassificationModel

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    Definition Classes
    PredictionModel
  112. def setProbabilityCol(value: String): GBTClassificationModel

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  113. def setRawPredictionCol(value: String): GBTClassificationModel

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    Definition Classes
    ClassificationModel
  114. def setThresholds(value: Array[Double]): GBTClassificationModel

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  115. final val stepSize: DoubleParam

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    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
  116. final val subsamplingRate: DoubleParam

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    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
  117. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  118. final val thresholds: DoubleArrayParam

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    Param for Thresholds in multi-class classification to adjust the probability of predicting each class.

    Param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.

    Definition Classes
    HasThresholds
  119. def toDebugString: String

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    Full description of model

    Full description of model

    Definition Classes
    TreeEnsembleModel
  120. def toString(): String

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    Summary of the model

    Summary of the model

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

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    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
  122. def transform(dataset: Dataset[_]): DataFrame

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    Transforms dataset by reading from featuresCol, and appending new columns as specified by parameters:

    Transforms dataset by reading from featuresCol, and appending new columns as specified by parameters:

    dataset

    input dataset

    returns

    transformed dataset

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

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    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" )
  124. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

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    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()
  125. def transformImpl(dataset: Dataset[_]): DataFrame

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    Attributes
    protected
    Definition Classes
    GBTClassificationModelPredictionModel
  126. def transformSchema(schema: StructType): StructType

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    :: DeveloperApi ::

    :: DeveloperApi ::

    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
    PredictionModelPipelineStage
  127. def transformSchema(schema: StructType, logging: Boolean): StructType

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    :: 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()
  128. def treeWeights: Array[Double]

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    Weights for each tree, zippable with trees

    Weights for each tree, zippable with trees

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

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    Trees in this ensemble.

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

    Definition Classes
    GBTClassificationModel → TreeEnsembleModel
    Annotations
    @Since( "1.4.0" )
  130. val uid: String

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    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    GBTClassificationModelIdentifiable
    Annotations
    @Since( "1.6.0" )
  131. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

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    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
    ProbabilisticClassifierParams → ClassifierParams → PredictorParams
  132. final val validationIndicatorCol: Param[String]

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    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
  133. final val validationTol: DoubleParam

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    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

  134. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  135. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  136. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  137. def write: MLWriter

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    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    GBTClassificationModelMLWritable
    Annotations
    @Since( "2.0.0" )

Deprecated Value Members

  1. def setCacheNodeIds(value: Boolean): GBTClassificationModel.this.type

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    Definition Classes
    DecisionTreeParams
    Annotations
    @deprecated
    Deprecated

    (Since version 2.1.0) This method is deprecated and will be removed in 3.0.0.

  2. def setCheckpointInterval(value: Int): GBTClassificationModel.this.type

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    Definition Classes
    DecisionTreeParams
    Annotations
    @deprecated
    Deprecated

    (Since version 2.1.0) This method is deprecated and will be removed in 3.0.0.

  3. def setFeatureSubsetStrategy(value: String): GBTClassificationModel.this.type

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    Definition Classes
    TreeEnsembleParams
    Annotations
    @deprecated
    Deprecated

    (Since version 2.1.0) This method is deprecated and will be removed in 3.0.0.

  4. def setImpurity(value: String): GBTClassificationModel.this.type

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    Definition Classes
    TreeClassifierParams
    Annotations
    @deprecated
    Deprecated

    (Since version 2.1.0) This method is deprecated and will be removed in 3.0.0.

  5. def setMaxBins(value: Int): GBTClassificationModel.this.type

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    Definition Classes
    DecisionTreeParams
    Annotations
    @deprecated
    Deprecated

    (Since version 2.1.0) This method is deprecated and will be removed in 3.0.0.

  6. def setMaxDepth(value: Int): GBTClassificationModel.this.type

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    Definition Classes
    DecisionTreeParams
    Annotations
    @deprecated
    Deprecated

    (Since version 2.1.0) This method is deprecated and will be removed in 3.0.0.

  7. def setMaxIter(value: Int): GBTClassificationModel.this.type

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    Definition Classes
    GBTParams
    Annotations
    @deprecated
    Deprecated

    (Since version 2.1.0) This method is deprecated and will be removed in 3.0.0.

  8. def setMaxMemoryInMB(value: Int): GBTClassificationModel.this.type

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    Definition Classes
    DecisionTreeParams
    Annotations
    @deprecated
    Deprecated

    (Since version 2.1.0) This method is deprecated and will be removed in 3.0.0.

  9. def setMinInfoGain(value: Double): GBTClassificationModel.this.type

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    Definition Classes
    DecisionTreeParams
    Annotations
    @deprecated
    Deprecated

    (Since version 2.1.0) This method is deprecated and will be removed in 3.0.0.

  10. def setMinInstancesPerNode(value: Int): GBTClassificationModel.this.type

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    Definition Classes
    DecisionTreeParams
    Annotations
    @deprecated
    Deprecated

    (Since version 2.1.0) This method is deprecated and will be removed in 3.0.0.

  11. def setSeed(value: Long): GBTClassificationModel.this.type

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    Definition Classes
    DecisionTreeParams
    Annotations
    @deprecated
    Deprecated

    (Since version 2.1.0) This method is deprecated and will be removed in 3.0.0.

  12. def setStepSize(value: Double): GBTClassificationModel.this.type

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    Definition Classes
    GBTParams
    Annotations
    @deprecated
    Deprecated

    (Since version 2.1.0) This method is deprecated and will be removed in 3.0.0.

  13. def setSubsamplingRate(value: Double): GBTClassificationModel.this.type

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    Definition Classes
    TreeEnsembleParams
    Annotations
    @deprecated
    Deprecated

    (Since version 2.1.0) This method is deprecated and will be removed in 3.0.0.

Inherited from MLWritable

Inherited from TreeEnsembleModel[DecisionTreeRegressionModel]

Inherited from GBTClassifierParams

Inherited from TreeClassifierParams

Inherited from GBTParams

Inherited from HasValidationIndicatorCol

Inherited from HasStepSize

Inherited from HasMaxIter

Inherited from TreeEnsembleParams

Inherited from DecisionTreeParams

Inherited from HasSeed

Inherited from HasCheckpointInterval

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Model[GBTClassificationModel]

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 setters

(expert-only) Parameter getters