Class/Object

ml.dmlc.xgboost4j.scala.spark

XGBoostClassifier

Related Docs: object XGBoostClassifier | package spark

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class XGBoostClassifier extends ProbabilisticClassifier[Vector, XGBoostClassifier, XGBoostClassificationModel] with XGBoostClassifierParams with DefaultParamsWritable

Linear Supertypes
DefaultParamsWritable, MLWritable, XGBoostClassifierParams, HasContribPredictionCol, HasLeafPredictionCol, ParamMapFuncs, HasNumClass, HasBaseMarginCol, HasWeightCol, BoosterParams, LearningTaskParams, GeneralParams, ProbabilisticClassifier[Vector, XGBoostClassifier, XGBoostClassificationModel], ProbabilisticClassifierParams, HasThresholds, HasProbabilityCol, Classifier[Vector, XGBoostClassifier, XGBoostClassificationModel], ClassifierParams, HasRawPredictionCol, Predictor[Vector, XGBoostClassifier, XGBoostClassificationModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Estimator[XGBoostClassificationModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. XGBoostClassifier
  2. DefaultParamsWritable
  3. MLWritable
  4. XGBoostClassifierParams
  5. HasContribPredictionCol
  6. HasLeafPredictionCol
  7. ParamMapFuncs
  8. HasNumClass
  9. HasBaseMarginCol
  10. HasWeightCol
  11. BoosterParams
  12. LearningTaskParams
  13. GeneralParams
  14. ProbabilisticClassifier
  15. ProbabilisticClassifierParams
  16. HasThresholds
  17. HasProbabilityCol
  18. Classifier
  19. ClassifierParams
  20. HasRawPredictionCol
  21. Predictor
  22. PredictorParams
  23. HasPredictionCol
  24. HasFeaturesCol
  25. HasLabelCol
  26. Estimator
  27. PipelineStage
  28. Logging
  29. Params
  30. Serializable
  31. Serializable
  32. Identifiable
  33. AnyRef
  34. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new XGBoostClassifier(xgboostParams: Map[String, Any])

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  2. new XGBoostClassifier(uid: String)

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  3. new XGBoostClassifier()

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  4. new XGBoostClassifier(uid: String, xgboostParams: Map[String, Any])

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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 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
  12. 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
  13. final def clear(param: Param[_]): XGBoostClassifier.this.type

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    Definition Classes
    Params
  14. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  15. 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
  16. 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
  17. final val contribPredictionCol: Param[String]

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    Param for contribution prediction column name.

    Param for contribution prediction column name.

    Definition Classes
    HasContribPredictionCol
  18. def copy(extra: ParamMap): XGBoostClassifier

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    Definition Classes
    XGBoostClassifier → Predictor → Estimator → PipelineStage → Params
  19. def copyValues[T <: Params](to: T, extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params
  20. 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
  21. 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
  22. final def defaultCopy[T <: Params](extra: ParamMap): T

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

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

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    Definition Classes
    AnyRef → Any
  25. 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
  26. 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
  27. def explainParam(param: Param[_]): String

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    Definition Classes
    Params
  28. def explainParams(): String

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    Definition Classes
    Params
  29. def extractLabeledPoints(dataset: Dataset[_], numClasses: Int): RDD[org.apache.spark.ml.feature.LabeledPoint]

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    Attributes
    protected
    Definition Classes
    Classifier
  30. def extractLabeledPoints(dataset: Dataset[_]): RDD[org.apache.spark.ml.feature.LabeledPoint]

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    Attributes
    protected
    Definition Classes
    Predictor
  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 finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  35. def fit(dataset: Dataset[_]): XGBoostClassificationModel

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

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

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

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  39. 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
  40. final def get[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  41. final def getAlpha: Double

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    Definition Classes
    BoosterParams
  42. final def getBaseMarginCol: String

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    Definition Classes
    HasBaseMarginCol
  43. final def getBaseScore: Double

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    Definition Classes
    LearningTaskParams
  44. final def getCheckpointInterval: Int

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    Definition Classes
    GeneralParams
  45. final def getCheckpointPath: String

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

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

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    Definition Classes
    BoosterParams
  48. final def getColsampleBytree: Double

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    Definition Classes
    BoosterParams
  49. final def getContribPredictionCol: String

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

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    Definition Classes
    Params
  51. final def getEta: Double

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    Definition Classes
    BoosterParams
  52. final def getEvalMetric: String

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    Definition Classes
    LearningTaskParams
  53. final def getFeaturesCol: String

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    Definition Classes
    HasFeaturesCol
  54. final def getGamma: Double

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    Definition Classes
    BoosterParams
  55. final def getGrowPolicy: 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 getMinChildWeight: Double

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    Definition Classes
    BoosterParams
  64. final def getMissing: Float

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    Definition Classes
    GeneralParams
  65. final def getNormalizeType: String

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    Definition Classes
    BoosterParams
  66. final def getNthread: Int

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    Definition Classes
    GeneralParams
  67. final def getNumClass: Int

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    Definition Classes
    HasNumClass
  68. def getNumClasses(dataset: Dataset[_], maxNumClasses: Int): Int

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    Attributes
    protected
    Definition Classes
    Classifier
  69. final def getNumEarlyStoppingRounds: Int

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    Definition Classes
    LearningTaskParams
  70. final def getNumRound: Int

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    Definition Classes
    GeneralParams
  71. final def getNumWorkers: Int

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    Definition Classes
    GeneralParams
  72. final def getObjective: String

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

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

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    Definition Classes
    Params
  75. final def getPredictionCol: String

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    Definition Classes
    HasPredictionCol
  76. final def getProbabilityCol: String

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    Definition Classes
    HasProbabilityCol
  77. final def getRateDrop: Double

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    Definition Classes
    BoosterParams
  78. final def getRawPredictionCol: String

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    Definition Classes
    HasRawPredictionCol
  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. def getThresholds: Array[Double]

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    Definition Classes
    HasThresholds
  87. final def getTimeoutRequestWorkers: Long

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    Definition Classes
    GeneralParams
  88. final def getTrainTestRatio: Double

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    Definition Classes
    LearningTaskParams
  89. final def getTreeLimit: Double

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    Definition Classes
    BoosterParams
  90. final def getTreeMethod: String

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    Definition Classes
    BoosterParams
  91. final def getUseExternalMemory: Boolean

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    Definition Classes
    GeneralParams
  92. final def getWeightCol: String

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

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    growth policy for fast histogram algorithm

    growth policy for fast histogram algorithm

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

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

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    Definition Classes
    Params
  96. def hashCode(): Int

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

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

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

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    Params
  100. final def isInstanceOf[T0]: Boolean

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

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    Definition Classes
    Params
  102. def isTraceEnabled(): Boolean

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

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    Definition Classes
    HasLabelCol
  104. 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
  105. 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
  106. final val leafPredictionCol: Param[String]

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    Param for leaf prediction column name.

    Param for leaf prediction column name.

    Definition Classes
    HasLeafPredictionCol
  107. def log: Logger

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

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

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

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

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

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    Attributes
    protected
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    Logging
  113. def logInfo(msg: ⇒ String): Unit

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    protected
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    Logging
  114. def logName: String

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

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

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

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

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

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    maximum number of bins in histogram

    maximum number of bins in histogram

    Definition Classes
    BoosterParams
  120. 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
  121. 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
  122. 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
  123. final val missing: FloatParam

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    the value treated as missing.

    the value treated as missing. default: Float.NaN

    Definition Classes
    GeneralParams
  124. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  125. 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
  126. final def notify(): Unit

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

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    Definition Classes
    AnyRef
  128. 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
  129. final val numClass: IntParam

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    number of classes

    number of classes

    Definition Classes
    HasNumClass
  130. 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
  131. final val numRound: IntParam

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    The number of rounds for boosting

    The number of rounds for boosting

    Definition Classes
    GeneralParams
  132. 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
  133. 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:linear, reg:logistic, binary:logistic, binary:logitraw, count:poisson, multi:softmax, multi:softprob, rank:pairwise, reg:gamma. default: reg:linear

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

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    Definition Classes
    Params
  135. final val predictionCol: Param[String]

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    Definition Classes
    HasPredictionCol
  136. final val probabilityCol: Param[String]

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    Definition Classes
    HasProbabilityCol
  137. 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
  138. final val rawPredictionCol: Param[String]

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    Definition Classes
    HasRawPredictionCol
  139. 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
  140. def save(path: String): Unit

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    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  141. 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
  142. 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
  143. final def set(paramPair: ParamPair[_]): XGBoostClassifier.this.type

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

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

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    Definition Classes
    Params
  146. def setAlpha(value: Double): XGBoostClassifier.this.type

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  147. def setBaseMarginCol(value: String): XGBoostClassifier.this.type

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  148. def setBaseScore(value: Double): XGBoostClassifier.this.type

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  149. def setCheckpointInterval(value: Int): XGBoostClassifier.this.type

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  150. def setCheckpointPath(value: String): XGBoostClassifier.this.type

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  151. def setColsampleBylevel(value: Double): XGBoostClassifier.this.type

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  152. def setColsampleBytree(value: Double): XGBoostClassifier.this.type

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  153. def setCustomEval(value: EvalTrait): XGBoostClassifier.this.type

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  154. def setCustomObj(value: ObjectiveTrait): XGBoostClassifier.this.type

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

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

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    Attributes
    protected
    Definition Classes
    Params
  157. def setEta(value: Double): XGBoostClassifier.this.type

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  158. def setEvalMetric(value: String): XGBoostClassifier.this.type

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  159. def setFeaturesCol(value: String): XGBoostClassifier

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    Definition Classes
    Predictor
  160. def setGamma(value: Double): XGBoostClassifier.this.type

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  161. def setGrowPolicy(value: String): XGBoostClassifier.this.type

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  162. def setLabelCol(value: String): XGBoostClassifier

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    Definition Classes
    Predictor
  163. def setLambda(value: Double): XGBoostClassifier.this.type

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  164. def setLambdaBias(value: Double): XGBoostClassifier.this.type

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  165. def setMaxBins(value: Int): XGBoostClassifier.this.type

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  166. def setMaxDeltaStep(value: Double): XGBoostClassifier.this.type

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  167. def setMaxDepth(value: Int): XGBoostClassifier.this.type

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  168. def setMinChildWeight(value: Double): XGBoostClassifier.this.type

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  169. def setMissing(value: Float): XGBoostClassifier.this.type

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  170. def setNormalizeType(value: String): XGBoostClassifier.this.type

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  171. def setNthread(value: Int): XGBoostClassifier.this.type

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  172. def setNumClass(value: Int): XGBoostClassifier.this.type

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  173. def setNumEarlyStoppingRounds(value: Int): XGBoostClassifier.this.type

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  174. def setNumRound(value: Int): XGBoostClassifier.this.type

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  175. def setNumWorkers(value: Int): XGBoostClassifier.this.type

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  176. def setObjective(value: String): XGBoostClassifier.this.type

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  177. def setPredictionCol(value: String): XGBoostClassifier

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    Definition Classes
    Predictor
  178. def setProbabilityCol(value: String): XGBoostClassifier

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    Definition Classes
    ProbabilisticClassifier
  179. def setRateDrop(value: Double): XGBoostClassifier.this.type

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  180. def setRawPredictionCol(value: String): XGBoostClassifier

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    Definition Classes
    Classifier
  181. def setSampleType(value: String): XGBoostClassifier.this.type

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  182. def setScalePosWeight(value: Double): XGBoostClassifier.this.type

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  183. def setSeed(value: Long): XGBoostClassifier.this.type

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  184. def setSilent(value: Int): XGBoostClassifier.this.type

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  185. def setSketchEps(value: Double): XGBoostClassifier.this.type

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  186. def setSkipDrop(value: Double): XGBoostClassifier.this.type

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  187. def setSubsample(value: Double): XGBoostClassifier.this.type

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  188. def setThresholds(value: Array[Double]): XGBoostClassifier

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    Definition Classes
    ProbabilisticClassifier
  189. def setTimeoutRequestWorkers(value: Long): XGBoostClassifier.this.type

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  190. def setTrainTestRatio(value: Double): XGBoostClassifier.this.type

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  191. def setTreeMethod(value: String): XGBoostClassifier.this.type

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  192. def setUseExternalMemory(value: Boolean): XGBoostClassifier.this.type

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  193. def setWeightCol(value: String): XGBoostClassifier.this.type

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  194. final val silent: IntParam

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    0 means printing running messages, 1 means silent mode.

    0 means printing running messages, 1 means silent mode. default: 0

    Definition Classes
    GeneralParams
  195. 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
  196. 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
  197. 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
  198. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  199. final val thresholds: DoubleArrayParam

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    Definition Classes
    HasThresholds
  200. 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
  201. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any
  202. 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
  203. def train(dataset: Dataset[_]): XGBoostClassificationModel

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    Attributes
    protected
    Definition Classes
    XGBoostClassifier → Predictor
  204. 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
  205. def transformSchema(schema: StructType): StructType

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

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

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    Definition Classes
    BoosterParams
  208. 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
  209. val uid: String

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    Definition Classes
    XGBoostClassifier → Identifiable
  210. 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
  211. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

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    Attributes
    protected
    Definition Classes
    ProbabilisticClassifierParams → ClassifierParams → PredictorParams
  212. final def wait(): Unit

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

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

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

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    Definition Classes
    HasWeightCol
  216. def write: MLWriter

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    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from XGBoostClassifierParams

Inherited from HasContribPredictionCol

Inherited from HasLeafPredictionCol

Inherited from ParamMapFuncs

Inherited from HasNumClass

Inherited from HasBaseMarginCol

Inherited from HasWeightCol

Inherited from BoosterParams

Inherited from LearningTaskParams

Inherited from GeneralParams

Inherited from ProbabilisticClassifier[Vector, XGBoostClassifier, XGBoostClassificationModel]

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from Classifier[Vector, XGBoostClassifier, XGBoostClassificationModel]

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from Predictor[Vector, XGBoostClassifier, XGBoostClassificationModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Estimator[XGBoostClassificationModel]

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