Class/Object

ml.dmlc.xgboost4j.scala.spark

XGBoostRegressionModel

Related Docs: object XGBoostRegressionModel | package spark

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class XGBoostRegressionModel extends PredictionModel[Vector, XGBoostRegressionModel] with XGBoostRegressorParams with MLWritable with Serializable

Linear Supertypes
MLWritable, XGBoostRegressorParams, NonParamVariables, HasContribPredictionCol, HasLeafPredictionCol, ParamMapFuncs, HasGroupCol, HasWeightCol, HasBaseMarginCol, LearningTaskParams, BoosterParams, GeneralParams, PredictionModel[Vector, XGBoostRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Model[XGBoostRegressionModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. XGBoostRegressionModel
  2. MLWritable
  3. XGBoostRegressorParams
  4. NonParamVariables
  5. HasContribPredictionCol
  6. HasLeafPredictionCol
  7. ParamMapFuncs
  8. HasGroupCol
  9. HasWeightCol
  10. HasBaseMarginCol
  11. LearningTaskParams
  12. BoosterParams
  13. GeneralParams
  14. PredictionModel
  15. PredictorParams
  16. HasPredictionCol
  17. HasFeaturesCol
  18. HasLabelCol
  19. Model
  20. Transformer
  21. PipelineStage
  22. Logging
  23. Params
  24. Serializable
  25. Serializable
  26. Identifiable
  27. AnyRef
  28. Any
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Visibility
  1. Public
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Instance Constructors

  1. new XGBoostRegressionModel(uid: String)

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

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    Definition Classes
    AnyRef → Any
  5. def MLlib2XGBoostParams: Map[String, Any]

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    Definition Classes
    ParamMapFuncs
  6. def XGBoostToMLlibParams(xgboostParams: Map[String, Any]): Unit

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    Definition Classes
    ParamMapFuncs
  7. final val alpha: DoubleParam

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    L1 regularization term on weights, increase this value will make model more conservative.

    L1 regularization term on weights, increase this value will make model more conservative. [default=0]

    Definition Classes
    BoosterParams
  8. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  9. final val baseMarginCol: Param[String]

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    Param for initial prediction (aka base margin) column name.

    Param for initial prediction (aka base margin) column name.

    Definition Classes
    HasBaseMarginCol
  10. final val baseScore: DoubleParam

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    the initial prediction score of all instances, global bias.

    the initial prediction score of all instances, global bias. default=0.5

    Definition Classes
    LearningTaskParams
  11. final val cacheTrainingSet: BooleanParam

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    whether caching training data

    whether caching training data

    Definition Classes
    LearningTaskParams
  12. 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 trained model will get checkpointed every 10 iterations. Note: checkpoint_path must also be set if the checkpoint interval is greater than 0.

    Definition Classes
    GeneralParams
  13. final val checkpointPath: Param[String]

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    The hdfs folder to load and save checkpoint boosters.

    The hdfs folder to load and save checkpoint boosters. default: empty_string

    Definition Classes
    GeneralParams
  14. final def clear(param: Param[_]): XGBoostRegressionModel.this.type

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    Definition Classes
    Params
  15. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  16. final val colsampleBylevel: DoubleParam

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    subsample ratio of columns for each split, in each level.

    subsample ratio of columns for each split, in each level. [default=1] range: (0,1]

    Definition Classes
    BoosterParams
  17. final val colsampleBytree: DoubleParam

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    subsample ratio of columns when constructing each tree.

    subsample ratio of columns when constructing each tree. [default=1] range: (0,1]

    Definition Classes
    BoosterParams
  18. final val contribPredictionCol: Param[String]

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    Param for contribution prediction column name.

    Param for contribution prediction column name.

    Definition Classes
    HasContribPredictionCol
  19. def copy(extra: ParamMap): XGBoostRegressionModel

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    Definition Classes
    XGBoostRegressionModel → Model → Transformer → PipelineStage → Params
  20. def copyValues[T <: Params](to: T, extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params
  21. final val customEval: CustomEvalParam

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    customized evaluation function provided by user.

    customized evaluation function provided by user. default: null

    Definition Classes
    GeneralParams
  22. final val customObj: CustomObjParam

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    customized objective function provided by user.

    customized objective function provided by user. default: null

    Definition Classes
    GeneralParams
  23. final def defaultCopy[T <: Params](extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params
  24. final def eq(arg0: AnyRef): Boolean

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

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    Definition Classes
    AnyRef → Any
  26. final val eta: DoubleParam

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    step size shrinkage used in update to prevents overfitting.

    step size shrinkage used in update to prevents overfitting. After each boosting step, we can directly get the weights of new features and eta actually shrinks the feature weights to make the boosting process more conservative. [default=0.3] range: [0,1]

    Definition Classes
    BoosterParams
  27. final val evalMetric: Param[String]

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    evaluation metrics for validation data, a default metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking).

    evaluation metrics for validation data, a default metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking). options: rmse, mae, logloss, error, merror, mlogloss, auc, aucpr, ndcg, map, gamma-deviance

    Definition Classes
    LearningTaskParams
  28. var evalSetsMap: Map[String, DataFrame]

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    Attributes
    protected
    Definition Classes
    NonParamVariables
  29. def explainParam(param: Param[_]): String

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    Definition Classes
    Params
  30. def explainParams(): String

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

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

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    Definition Classes
    Params
  33. final val featuresCol: Param[String]

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    Definition Classes
    HasFeaturesCol
  34. def featuresDataType: DataType

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    Attributes
    protected
    Definition Classes
    PredictionModel
  35. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  36. final val gamma: DoubleParam

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    minimum loss reduction required to make a further partition on a leaf node of the tree.

    minimum loss reduction required to make a further partition on a leaf node of the tree. the larger, the more conservative the algorithm will be. [default=0] range: [0, Double.MaxValue]

    Definition Classes
    BoosterParams
  37. final def get[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  38. final def getAlpha: Double

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    Definition Classes
    BoosterParams
  39. final def getBaseMarginCol: String

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    Definition Classes
    HasBaseMarginCol
  40. final def getBaseScore: Double

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    Definition Classes
    LearningTaskParams
  41. final def getCheckpointInterval: Int

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    Definition Classes
    GeneralParams
  42. final def getCheckpointPath: String

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    Definition Classes
    GeneralParams
  43. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  44. final def getColsampleBylevel: Double

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    Definition Classes
    BoosterParams
  45. final def getColsampleBytree: Double

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    Definition Classes
    BoosterParams
  46. final def getContribPredictionCol: String

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    Definition Classes
    HasContribPredictionCol
  47. final def getDefault[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  48. final def getEta: Double

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    Definition Classes
    BoosterParams
  49. final def getEvalMetric: String

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    Definition Classes
    LearningTaskParams
  50. def getEvalSets(params: Map[String, Any]): Map[String, DataFrame]

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    Definition Classes
    NonParamVariables
  51. final def getFeaturesCol: String

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    Definition Classes
    HasFeaturesCol
  52. final def getGamma: Double

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    Definition Classes
    BoosterParams
  53. final def getGroupCol: String

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    Definition Classes
    HasGroupCol
  54. final def getGrowPolicy: String

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    Definition Classes
    BoosterParams
  55. final def getInteractionConstraints: String

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    Definition Classes
    BoosterParams
  56. final def getLabelCol: String

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    Definition Classes
    HasLabelCol
  57. final def getLambda: Double

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    Definition Classes
    BoosterParams
  58. final def getLambdaBias: Double

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    Definition Classes
    BoosterParams
  59. final def getLeafPredictionCol: String

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    Definition Classes
    HasLeafPredictionCol
  60. final def getMaxBins: Int

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    Definition Classes
    BoosterParams
  61. final def getMaxDeltaStep: Double

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    Definition Classes
    BoosterParams
  62. final def getMaxDepth: Int

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    Definition Classes
    BoosterParams
  63. final def getMaxLeaves: Int

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    Definition Classes
    BoosterParams
  64. final def getMaximizeEvaluationMetrics: Boolean

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    Definition Classes
    LearningTaskParams
  65. final def getMinChildWeight: Double

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    Definition Classes
    BoosterParams
  66. final def getMissing: Float

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    Definition Classes
    GeneralParams
  67. final def getMonotoneConstraints: String

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    Definition Classes
    BoosterParams
  68. final def getNormalizeType: String

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    Definition Classes
    BoosterParams
  69. final def getNthread: Int

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    Definition Classes
    GeneralParams
  70. final def getNumEarlyStoppingRounds: Int

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    Definition Classes
    LearningTaskParams
  71. final def getNumRound: Int

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    Definition Classes
    GeneralParams
  72. final def getNumWorkers: Int

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    Definition Classes
    GeneralParams
  73. final def getObjective: String

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    Definition Classes
    LearningTaskParams
  74. final def getObjectiveType: String

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    Definition Classes
    LearningTaskParams
  75. final def getOrDefault[T](param: Param[T]): T

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

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    Definition Classes
    Params
  77. final def getPredictionCol: String

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    Definition Classes
    HasPredictionCol
  78. final def getRateDrop: Double

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    Definition Classes
    BoosterParams
  79. final def getSampleType: String

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    Definition Classes
    BoosterParams
  80. final def getScalePosWeight: Double

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    Definition Classes
    BoosterParams
  81. final def getSeed: Long

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    Definition Classes
    GeneralParams
  82. final def getSilent: Int

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    Definition Classes
    GeneralParams
  83. final def getSketchEps: Double

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    Definition Classes
    BoosterParams
  84. final def getSkipDrop: Double

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    Definition Classes
    BoosterParams
  85. final def getSubsample: Double

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    Definition Classes
    BoosterParams
  86. final def getTimeoutRequestWorkers: Long

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    Definition Classes
    GeneralParams
  87. final def getTrainTestRatio: Double

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    Definition Classes
    LearningTaskParams
  88. final def getTreeLimit: Int

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    Definition Classes
    BoosterParams
  89. final def getTreeMethod: String

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    Definition Classes
    BoosterParams
  90. final def getUseExternalMemory: Boolean

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    Definition Classes
    GeneralParams
  91. final def getVerbosity: Int

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    Definition Classes
    GeneralParams
  92. final def getWeightCol: String

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    Definition Classes
    HasWeightCol
  93. final val groupCol: Param[String]

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    Param for group column name.

    Param for group column name.

    Definition Classes
    HasGroupCol
  94. final val growPolicy: Param[String]

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    growth policy for fast histogram algorithm

    growth policy for fast histogram algorithm

    Definition Classes
    BoosterParams
  95. final def hasDefault[T](param: Param[T]): Boolean

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

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    Definition Classes
    Params
  97. def hasParent: Boolean

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    Definition Classes
    Model
  98. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  99. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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

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    Attributes
    protected
    Definition Classes
    Logging
  101. final val interactionConstraints: Param[String]

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    Definition Classes
    BoosterParams
  102. final def isDefined(param: Param[_]): Boolean

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    Params
  103. final def isInstanceOf[T0]: Boolean

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    Any
  104. final def isSet(param: Param[_]): Boolean

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    Definition Classes
    Params
  105. def isTraceEnabled(): Boolean

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    Attributes
    protected
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    Logging
  106. final val labelCol: Param[String]

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    Definition Classes
    HasLabelCol
  107. final val lambda: DoubleParam

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    L2 regularization term on weights, increase this value will make model more conservative.

    L2 regularization term on weights, increase this value will make model more conservative. [default=1]

    Definition Classes
    BoosterParams
  108. final val lambdaBias: DoubleParam

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    Parameter of linear booster L2 regularization term on bias, default 0(no L1 reg on bias because it is not important)

    Parameter of linear booster L2 regularization term on bias, default 0(no L1 reg on bias because it is not important)

    Definition Classes
    BoosterParams
  109. final val leafPredictionCol: Param[String]

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    Param for leaf prediction column name.

    Param for leaf prediction column name.

    Definition Classes
    HasLeafPredictionCol
  110. def log: Logger

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    Logging
  111. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    Logging
  112. def logDebug(msg: ⇒ String): Unit

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    Logging
  113. def logError(msg: ⇒ String, throwable: Throwable): Unit

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

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

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

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    protected
    Definition Classes
    Logging
  117. def logName: String

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    protected
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    Logging
  118. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    protected
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    Logging
  119. def logTrace(msg: ⇒ String): Unit

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    protected
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    Logging
  120. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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

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    Attributes
    protected
    Definition Classes
    Logging
  122. final val maxBins: IntParam

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    maximum number of bins in histogram

    maximum number of bins in histogram

    Definition Classes
    BoosterParams
  123. final val maxDeltaStep: DoubleParam

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    Maximum delta step we allow each tree's weight estimation to be.

    Maximum delta step we allow each tree's weight estimation to be. If the value is set to 0, it means there is no constraint. If it is set to a positive value, it can help making the update step more conservative. Usually this parameter is not needed, but it might help in logistic regression when class is extremely imbalanced. Set it to value of 1-10 might help control the update. [default=0] range: [0, Double.MaxValue]

    Definition Classes
    BoosterParams
  124. final val maxDepth: IntParam

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    maximum depth of a tree, increase this value will make model more complex / likely to be overfitting.

    maximum depth of a tree, increase this value will make model more complex / likely to be overfitting. [default=6] range: [1, Int.MaxValue]

    Definition Classes
    BoosterParams
  125. final val maxLeaves: IntParam

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    Maximum number of nodes to be added.

    Maximum number of nodes to be added. Only relevant when grow_policy=lossguide is set.

    Definition Classes
    BoosterParams
  126. final val maximizeEvaluationMetrics: BooleanParam

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    Definition Classes
    LearningTaskParams
  127. final val minChildWeight: DoubleParam

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    minimum sum of instance weight(hessian) needed in a child.

    minimum sum of instance weight(hessian) needed in a child. If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight, then the building process will give up further partitioning. In linear regression mode, this simply corresponds to minimum number of instances needed to be in each node. The larger, the more conservative the algorithm will be. [default=1] range: [0, Double.MaxValue]

    Definition Classes
    BoosterParams
  128. final val missing: FloatParam

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    the value treated as missing.

    the value treated as missing. default: Float.NaN

    Definition Classes
    GeneralParams
  129. final val monotoneConstraints: Param[String]

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    Definition Classes
    BoosterParams
  130. def nativeBooster: Booster

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    Get the native booster instance of this model.

    Get the native booster instance of this model. This is used to call low-level APIs on native booster, such as "getFeatureScore".

  131. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  132. final val normalizeType: Param[String]

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    Parameter of Dart booster.

    Parameter of Dart booster. type of normalization algorithm, options: {'tree', 'forest'}. [default="tree"]

    Definition Classes
    BoosterParams
  133. final def notify(): Unit

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

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    Definition Classes
    AnyRef
  135. final val nthread: IntParam

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    number of threads used by per worker.

    number of threads used by per worker. default 1

    Definition Classes
    GeneralParams
  136. final val numEarlyStoppingRounds: IntParam

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    If non-zero, the training will be stopped after a specified number of consecutive increases in any evaluation metric.

    If non-zero, the training will be stopped after a specified number of consecutive increases in any evaluation metric.

    Definition Classes
    LearningTaskParams
  137. def numFeatures: Int

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    Definition Classes
    PredictionModel
    Annotations
    @Since( "1.6.0" )
  138. final val numRound: IntParam

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    The number of rounds for boosting

    The number of rounds for boosting

    Definition Classes
    GeneralParams
  139. final val numWorkers: IntParam

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    number of workers used to train xgboost model.

    number of workers used to train xgboost model. default: 1

    Definition Classes
    GeneralParams
  140. final val objective: Param[String]

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    Specify the learning task and the corresponding learning objective.

    Specify the learning task and the corresponding learning objective. options: reg:squarederror, reg:logistic, binary:logistic, binary:logitraw, count:poisson, multi:softmax, multi:softprob, rank:pairwise, reg:gamma. default: reg:squarederror

    Definition Classes
    LearningTaskParams
  141. final val objectiveType: Param[String]

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    The learning objective type of the specified custom objective and eval.

    The learning objective type of the specified custom objective and eval. Corresponding type will be assigned if custom objective is defined options: regression, classification. default: null

    Definition Classes
    LearningTaskParams
  142. lazy val params: Array[Param[_]]

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    Definition Classes
    Params
  143. var parent: Estimator[XGBoostRegressionModel]

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    Definition Classes
    Model
  144. def predict(features: Vector): Double

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    Single instance prediction.

    Single instance prediction. Note: The performance is not ideal, use it carefully!

    Definition Classes
    XGBoostRegressionModel → PredictionModel
  145. final val predictionCol: Param[String]

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    Definition Classes
    HasPredictionCol
  146. final val rateDrop: DoubleParam

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    Parameter of Dart booster.

    Parameter of Dart booster. dropout rate. [default=0.0] range: [0.0, 1.0]

    Definition Classes
    BoosterParams
  147. final val sampleType: Param[String]

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    Parameter for Dart booster.

    Parameter for Dart booster. Type of sampling algorithm. "uniform": dropped trees are selected uniformly. "weighted": dropped trees are selected in proportion to weight. [default="uniform"]

    Definition Classes
    BoosterParams
  148. def save(path: String): Unit

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    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  149. final val scalePosWeight: DoubleParam

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    Control the balance of positive and negative weights, useful for unbalanced classes.

    Control the balance of positive and negative weights, useful for unbalanced classes. A typical value to consider: sum(negative cases) / sum(positive cases). [default=1]

    Definition Classes
    BoosterParams
  150. final val seed: LongParam

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    Random seed for the C++ part of XGBoost and train/test splitting.

    Random seed for the C++ part of XGBoost and train/test splitting.

    Definition Classes
    GeneralParams
  151. final def set(paramPair: ParamPair[_]): XGBoostRegressionModel.this.type

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    Attributes
    protected
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    Params
  152. final def set(param: String, value: Any): XGBoostRegressionModel.this.type

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

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    Definition Classes
    Params
  154. def setContribPredictionCol(value: String): XGBoostRegressionModel.this.type

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  155. final def setDefault(paramPairs: ParamPair[_]*): XGBoostRegressionModel.this.type

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    protected
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    Params
  156. final def setDefault[T](param: Param[T], value: T): XGBoostRegressionModel.this.type

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    Attributes
    protected
    Definition Classes
    Params
  157. def setEvalSets(evalSets: Map[String, DataFrame]): XGBoostRegressionModel.this.type

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    Definition Classes
    NonParamVariables
  158. def setFeaturesCol(value: String): XGBoostRegressionModel

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    Definition Classes
    PredictionModel
  159. def setLeafPredictionCol(value: String): XGBoostRegressionModel.this.type

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  160. def setParent(parent: Estimator[XGBoostRegressionModel]): XGBoostRegressionModel

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    Definition Classes
    Model
  161. def setPredictionCol(value: String): XGBoostRegressionModel

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    Definition Classes
    PredictionModel
  162. def setTreeLimit(value: Int): XGBoostRegressionModel.this.type

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  163. final val silent: IntParam

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    Deprecated.

    Deprecated. Please use verbosity instead. 0 means printing running messages, 1 means silent mode. default: 0

    Definition Classes
    GeneralParams
  164. final val sketchEps: DoubleParam

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    This is only used for approximate greedy algorithm.

    This is only used for approximate greedy algorithm. This roughly translated into O(1 / sketch_eps) number of bins. Compared to directly select number of bins, this comes with theoretical guarantee with sketch accuracy. [default=0.03] range: (0, 1)

    Definition Classes
    BoosterParams
  165. final val skipDrop: DoubleParam

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    Parameter of Dart booster.

    Parameter of Dart booster. probability of skip dropout. If a dropout is skipped, new trees are added in the same manner as gbtree. [default=0.0] range: [0.0, 1.0]

    Definition Classes
    BoosterParams
  166. final val subsample: DoubleParam

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    subsample ratio of the training instance.

    subsample ratio of the training instance. Setting it to 0.5 means that XGBoost randomly collected half of the data instances to grow trees and this will prevent overfitting. [default=1] range:(0,1]

    Definition Classes
    BoosterParams
  167. def summary: XGBoostTrainingSummary

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    Returns summary (e.g.

    Returns summary (e.g. train/test objective history) of model on the training set. An exception is thrown if no summary is available.

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

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    Definition Classes
    AnyRef
  169. final val timeoutRequestWorkers: LongParam

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    the maximum time to wait for the job requesting new workers.

    the maximum time to wait for the job requesting new workers. default: 30 minutes

    Definition Classes
    GeneralParams
  170. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any
  171. final val trackerConf: TrackerConfParam

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    Rabit tracker configurations.

    Rabit tracker configurations. The parameter must be provided as an instance of the TrackerConf class, which has the following definition:

    case class TrackerConf(workerConnectionTimeout: Duration, trainingTimeout: Duration, trackerImpl: String)

    See below for detailed explanations.

    • trackerImpl: Select the implementation of Rabit tracker. default: "python"

    Choice between "python" or "scala". The former utilizes the Java wrapper of the Python Rabit tracker (in dmlc_core), and does not support timeout settings. The "scala" version removes Python components, and fully supports timeout settings.

    • workerConnectionTimeout: the maximum wait time for all workers to connect to the tracker. default: 0 millisecond (no timeout)

    The timeout value should take the time of data loading and pre-processing into account, due to the lazy execution of Spark's operations. Alternatively, you may force Spark to perform data transformation before calling XGBoost.train(), so that this timeout truly reflects the connection delay. Set a reasonable timeout value to prevent model training/testing from hanging indefinitely, possible due to network issues. Note that zero timeout value means to wait indefinitely (equivalent to Duration.Inf). Ignored if the tracker implementation is "python".

    Definition Classes
    GeneralParams
  172. final val trainTestRatio: DoubleParam

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    Fraction of training points to use for testing.

    Fraction of training points to use for testing.

    Definition Classes
    LearningTaskParams
  173. def transform(dataset: Dataset[_]): DataFrame

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    Definition Classes
    XGBoostRegressionModel → PredictionModel → Transformer
  174. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

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

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    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  176. def transformImpl(dataset: Dataset[_]): DataFrame

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    Attributes
    protected
    Definition Classes
    PredictionModel
  177. def transformSchema(schema: StructType): StructType

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    Definition Classes
    PredictionModel → PipelineStage
  178. def transformSchema(schema: StructType, logging: Boolean): StructType

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    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  179. final val treeLimit: IntParam

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    Definition Classes
    BoosterParams
  180. final val treeMethod: Param[String]

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    The tree construction algorithm used in XGBoost.

    The tree construction algorithm used in XGBoost. options: {'auto', 'exact', 'approx'} [default='auto']

    Definition Classes
    BoosterParams
  181. val uid: String

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    Definition Classes
    XGBoostRegressionModel → Identifiable
  182. final val useExternalMemory: BooleanParam

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    whether to use external memory as cache.

    whether to use external memory as cache. default: false

    Definition Classes
    GeneralParams
  183. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

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    Attributes
    protected
    Definition Classes
    PredictorParams
  184. final val verbosity: IntParam

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    Verbosity of printing messages.

    Verbosity of printing messages. Valid values are 0 (silent), 1 (warning), 2 (info), 3 (debug). default: 1

    Definition Classes
    GeneralParams
  185. final def wait(): Unit

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

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  188. final val weightCol: Param[String]

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    Definition Classes
    HasWeightCol
  189. def write: MLWriter

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    Definition Classes
    XGBoostRegressionModel → MLWritable

Inherited from MLWritable

Inherited from XGBoostRegressorParams

Inherited from NonParamVariables

Inherited from HasContribPredictionCol

Inherited from HasLeafPredictionCol

Inherited from ParamMapFuncs

Inherited from HasGroupCol

Inherited from HasWeightCol

Inherited from HasBaseMarginCol

Inherited from LearningTaskParams

Inherited from BoosterParams

Inherited from GeneralParams

Inherited from PredictionModel[Vector, XGBoostRegressionModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Model[XGBoostRegressionModel]

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

getParam

param

Ungrouped