ml.dmlc.xgboost4j.scala.spark

# XGBoostRegressionModel

###
Related Docs: object XGBoostRegressionModel | package spark

#### 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

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2. All

### Instance Constructors

1. #### new XGBoostRegressionModel(uid: String)

### Value Members

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

   Definition Classes
     AnyRef → Any

2. #### final def ##(): Int

   Definition Classes
     AnyRef → Any

3. #### final def $[T](param: Param[T]): T

   Attributes
     protected
   Definition Classes
     Params

4. #### final def ==(arg0: Any): Boolean

   Definition Classes
     AnyRef → Any

5. #### def MLlib2XGBoostParams: Map[String, Any]

   Definition Classes
     ParamMapFuncs

6. #### def XGBoostToMLlibParams(xgboostParams: Map[String, Any]): Unit

   Definition Classes
     ParamMapFuncs

7. #### final val alpha: DoubleParam

   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

   Definition Classes
     Any

9. #### final val baseMarginCol: Param[String]

   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

    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

    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]

    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[_]): XGBoostRegressionModel.this.type

    Definition Classes
      Params

14. #### def clone(): AnyRef

    Attributes
      protected[java.lang]
    Definition Classes
      AnyRef
    Annotations
      @throws( ... )

15. #### final val colsampleBylevel: DoubleParam

    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

    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]

    Param for contribution prediction column name.

    Param for contribution prediction column name.

    Definition Classes
      HasContribPredictionCol

18. #### def copy(extra: ParamMap): XGBoostRegressionModel

    Definition Classes
      XGBoostRegressionModel → Model → Transformer → PipelineStage → Params

19. #### def copyValues[T <: Params](to: T, extra: ParamMap): T

    Attributes
      protected
    Definition Classes
      Params

20. #### final val customEval: CustomEvalParam

    customized evaluation function provided by user.

    customized evaluation function provided by user. default: null

    Definition Classes
      GeneralParams

21. #### final val customObj: CustomObjParam

    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

    Attributes
      protected
    Definition Classes
      Params

23. #### final def eq(arg0: AnyRef): Boolean

    Definition Classes
      AnyRef

24. #### def equals(arg0: Any): Boolean

    Definition Classes
      AnyRef → Any

25. #### final val eta: DoubleParam

    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]

    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. #### var evalSetsMap: Map[String, DataFrame]

    Attributes
      protected
    Definition Classes
      NonParamVariables

28. #### def explainParam(param: Param[_]): String

    Definition Classes
      Params

29. #### def explainParams(): String

    Definition Classes
      Params

30. #### final def extractParamMap(): ParamMap

    Definition Classes
      Params

31. #### final def extractParamMap(extra: ParamMap): ParamMap

    Definition Classes
      Params

32. #### final val featuresCol: Param[String]

    Definition Classes
      HasFeaturesCol

33. #### def featuresDataType: DataType

    Attributes
      protected
    Definition Classes
      PredictionModel

34. #### def finalize(): Unit

    Attributes
      protected[java.lang]
    Definition Classes
      AnyRef
    Annotations
      @throws( classOf[java.lang.Throwable] )

35. #### final val gamma: DoubleParam

    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

36. #### final def get[T](param: Param[T]): Option[T]

    Definition Classes
      Params

37. #### final def getAlpha: Double

    Definition Classes
      BoosterParams

38. #### final def getBaseMarginCol: String

    Definition Classes
      HasBaseMarginCol

39. #### final def getBaseScore: Double

    Definition Classes
      LearningTaskParams

40. #### final def getCheckpointInterval: Int

    Definition Classes
      GeneralParams

41. #### final def getCheckpointPath: String

    Definition Classes
      GeneralParams

42. #### final def getClass(): Class[_]

    Definition Classes
      AnyRef → Any

43. #### final def getColsampleBylevel: Double

    Definition Classes
      BoosterParams

44. #### final def getColsampleBytree: Double

    Definition Classes
      BoosterParams

45. #### final def getContribPredictionCol: String

    Definition Classes
      HasContribPredictionCol

46. #### final def getDefault[T](param: Param[T]): Option[T]

    Definition Classes
      Params

47. #### final def getEta: Double

    Definition Classes
      BoosterParams

48. #### final def getEvalMetric: String

    Definition Classes
      LearningTaskParams

49. #### def getEvalSets(params: Map[String, Any]): Map[String, DataFrame]

    Definition Classes
      NonParamVariables

50. #### final def getFeaturesCol: String

    Definition Classes
      HasFeaturesCol

51. #### final def getGamma: Double

    Definition Classes
      BoosterParams

52. #### final def getGroupCol: String

    Definition Classes
      HasGroupCol

53. #### final def getGrowPolicy: String

    Definition Classes
      BoosterParams

54. #### final def getInteractionConstraints: String

    Definition Classes
      BoosterParams

55. #### final def getLabelCol: String

    Definition Classes
      HasLabelCol

56. #### final def getLambda: Double

    Definition Classes
      BoosterParams

57. #### final def getLambdaBias: Double

    Definition Classes
      BoosterParams

58. #### final def getLeafPredictionCol: String

    Definition Classes
      HasLeafPredictionCol

59. #### final def getMaxBins: Int

    Definition Classes
      BoosterParams

60. #### final def getMaxDeltaStep: Double

    Definition Classes
      BoosterParams

61. #### final def getMaxDepth: Int

    Definition Classes
      BoosterParams

62. #### final def getMaxLeaves: Int

    Definition Classes
      BoosterParams

63. #### final def getMaximizeEvaluationMetrics: Boolean

    Definition Classes
      LearningTaskParams

64. #### final def getMinChildWeight: Double

    Definition Classes
      BoosterParams

65. #### final def getMissing: Float

    Definition Classes
      GeneralParams

66. #### final def getMonotoneConstraints: String

    Definition Classes
      BoosterParams

67. #### final def getNormalizeType: String

    Definition Classes
      BoosterParams

68. #### final def getNthread: Int

    Definition Classes
      GeneralParams

69. #### final def getNumEarlyStoppingRounds: Int

    Definition Classes
      LearningTaskParams

70. #### final def getNumRound: Int

    Definition Classes
      GeneralParams

71. #### final def getNumWorkers: Int

    Definition Classes
      GeneralParams

72. #### final def getObjective: String

    Definition Classes
      LearningTaskParams

73. #### final def getObjectiveType: String

    Definition Classes
      LearningTaskParams

74. #### final def getOrDefault[T](param: Param[T]): T

    Definition Classes
      Params

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

    Definition Classes
      Params

76. #### final def getPredictionCol: String

    Definition Classes
      HasPredictionCol

77. #### final def getRateDrop: Double

    Definition Classes
      BoosterParams

78. #### final def getSampleType: String

    Definition Classes
      BoosterParams

79. #### final def getScalePosWeight: Double

    Definition Classes
      BoosterParams

80. #### final def getSeed: Long

    Definition Classes
      GeneralParams

81. #### final def getSilent: Int

    Definition Classes
      GeneralParams

82. #### final def getSketchEps: Double

    Definition Classes
      BoosterParams

83. #### final def getSkipDrop: Double

    Definition Classes
      BoosterParams

84. #### final def getSubsample: Double

    Definition Classes
      BoosterParams

85. #### final def getTimeoutRequestWorkers: Long

    Definition Classes
      GeneralParams

86. #### final def getTrainTestRatio: Double

    Definition Classes
      LearningTaskParams

87. #### final def getTreeLimit: Int

    Definition Classes
      BoosterParams

88. #### final def getTreeMethod: String

    Definition Classes
      BoosterParams

89. #### final def getUseExternalMemory: Boolean

    Definition Classes
      GeneralParams

90. #### final def getVerbosity: Int

    Definition Classes
      GeneralParams

91. #### final def getWeightCol: String

    Definition Classes
      HasWeightCol

92. #### final val groupCol: Param[String]

    Param for group column name.

    Param for group column name.

    Definition Classes
      HasGroupCol

93. #### final val growPolicy: Param[String]

    growth policy for fast histogram algorithm

    growth policy for fast histogram algorithm

    Definition Classes
      BoosterParams

94. #### final def hasDefault[T](param: Param[T]): Boolean

    Definition Classes
      Params

95. #### def hasParam(paramName: String): Boolean

    Definition Classes
      Params

96. #### def hasParent: Boolean

    Definition Classes
      Model

97. #### def hashCode(): Int

    Definition Classes
      AnyRef → Any

98. #### def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

    Attributes
      protected
    Definition Classes
      Logging

99. #### def initializeLogIfNecessary(isInterpreter: Boolean): Unit

    Attributes
      protected
    Definition Classes
      Logging

100. #### final val interactionConstraints: Param[String]

     Definition Classes
       BoosterParams

101. #### final def isDefined(param: Param[_]): Boolean

     Definition Classes
       Params

102. #### final def isInstanceOf[T0]: Boolean

     Definition Classes
       Any

103. #### final def isSet(param: Param[_]): Boolean

     Definition Classes
       Params

104. #### def isTraceEnabled(): Boolean

     Attributes
       protected
     Definition Classes
       Logging

105. #### final val labelCol: Param[String]

     Definition Classes
       HasLabelCol

106. #### final val lambda: DoubleParam

     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

107. #### final val lambdaBias: DoubleParam

     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

108. #### final val leafPredictionCol: Param[String]

     Param for leaf prediction column name.

     Param for leaf prediction column name.

     Definition Classes
       HasLeafPredictionCol

109. #### def log: Logger

     Attributes
       protected
     Definition Classes
       Logging

110. #### def logDebug(msg: ⇒ String, throwable: Throwable): Unit

     Attributes
       protected
     Definition Classes
       Logging

111. #### def logDebug(msg: ⇒ String): Unit

     Attributes
       protected
     Definition Classes
       Logging

112. #### def logError(msg: ⇒ String, throwable: Throwable): Unit

     Attributes
       protected
     Definition Classes
       Logging

113. #### def logError(msg: ⇒ String): Unit

     Attributes
       protected
     Definition Classes
       Logging

114. #### def logInfo(msg: ⇒ String, throwable: Throwable): Unit

     Attributes
       protected
     Definition Classes
       Logging

115. #### def logInfo(msg: ⇒ String): Unit

     Attributes
       protected
     Definition Classes
       Logging

116. #### def logName: String

     Attributes
       protected
     Definition Classes
       Logging

117. #### def logTrace(msg: ⇒ String, throwable: Throwable): Unit

     Attributes
       protected
     Definition Classes
       Logging

118. #### def logTrace(msg: ⇒ String): Unit

     Attributes
       protected
     Definition Classes
       Logging

119. #### def logWarning(msg: ⇒ String, throwable: Throwable): Unit

     Attributes
       protected
     Definition Classes
       Logging

120. #### def logWarning(msg: ⇒ String): Unit

     Attributes
       protected
     Definition Classes
       Logging

121. #### final val maxBins: IntParam

     maximum number of bins in histogram

     maximum number of bins in histogram

     Definition Classes
       BoosterParams

122. #### final val maxDeltaStep: DoubleParam

     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

123. #### final val maxDepth: IntParam

     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

124. #### final val maxLeaves: IntParam

     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

125. #### final val maximizeEvaluationMetrics: BooleanParam

     Definition Classes
       LearningTaskParams

126. #### final val minChildWeight: DoubleParam

     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

127. #### final val missing: FloatParam

     the value treated as missing.

     the value treated as missing. default: Float.NaN

     Definition Classes
       GeneralParams

128. #### final val monotoneConstraints: Param[String]

     Definition Classes
       BoosterParams

129. #### def nativeBooster: Booster

     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".

130. #### final def ne(arg0: AnyRef): Boolean

     Definition Classes
       AnyRef

131. #### final val normalizeType: Param[String]

     Parameter of Dart booster.

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

     Definition Classes
       BoosterParams

132. #### final def notify(): Unit

     Definition Classes
       AnyRef

133. #### final def notifyAll(): Unit

     Definition Classes
       AnyRef

134. #### final val nthread: IntParam

     number of threads used by per worker.

     number of threads used by per worker. default 1

     Definition Classes
       GeneralParams

135. #### final val numEarlyStoppingRounds: IntParam

     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

136. #### def numFeatures: Int

     Definition Classes
       PredictionModel
     Annotations
       @Since( "1.6.0" )

137. #### final val numRound: IntParam

     The number of rounds for boosting

     The number of rounds for boosting

     Definition Classes
       GeneralParams

138. #### final val numWorkers: IntParam

     number of workers used to train xgboost model.

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

     Definition Classes
       GeneralParams

139. #### final val objective: Param[String]

     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

140. #### final val objectiveType: Param[String]

     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

141. #### lazy val params: Array[Param[_]]

     Definition Classes
       Params

142. #### var parent: Estimator[XGBoostRegressionModel]

     Definition Classes
       Model

143. #### def predict(features: Vector): Double

     Single instance prediction.

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

     Definition Classes
       XGBoostRegressionModel → PredictionModel

144. #### final val predictionCol: Param[String]

     Definition Classes
       HasPredictionCol

145. #### final val rateDrop: DoubleParam

     Parameter of Dart booster.

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

     Definition Classes
       BoosterParams

146. #### final val sampleType: Param[String]

     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

147. #### def save(path: String): Unit

     Definition Classes
       MLWritable
     Annotations
       @Since( "1.6.0" ) @throws( ... )

148. #### final val scalePosWeight: DoubleParam

     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

149. #### final val seed: LongParam

     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

150. #### final def set(paramPair: ParamPair[_]): XGBoostRegressionModel.this.type

     Attributes
       protected
     Definition Classes
       Params

151. #### final def set(param: String, value: Any): XGBoostRegressionModel.this.type

     Attributes
       protected
     Definition Classes
       Params

152. #### final def set[T](param: Param[T], value: T): XGBoostRegressionModel.this.type

     Definition Classes
       Params

153. #### def setContribPredictionCol(value: String): XGBoostRegressionModel.this.type

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

     Attributes
       protected
     Definition Classes
       Params

155. #### final def setDefault[T](param: Param[T], value: T): XGBoostRegressionModel.this.type

     Attributes
       protected
     Definition Classes
       Params

156. #### def setEvalSets(evalSets: Map[String, DataFrame]): XGBoostRegressionModel.this.type

     Definition Classes
       NonParamVariables

157. #### def setFeaturesCol(value: String): XGBoostRegressionModel

     Definition Classes
       PredictionModel

158. #### def setLeafPredictionCol(value: String): XGBoostRegressionModel.this.type

159. #### def setParent(parent: Estimator[XGBoostRegressionModel]): XGBoostRegressionModel

     Definition Classes
       Model

160. #### def setPredictionCol(value: String): XGBoostRegressionModel

     Definition Classes
       PredictionModel

161. #### def setTreeLimit(value: Int): XGBoostRegressionModel.this.type

162. #### final val silent: IntParam

     Deprecated.

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

     Definition Classes
       GeneralParams

163. #### final val sketchEps: DoubleParam

     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

164. #### final val skipDrop: DoubleParam

     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

165. #### final val subsample: DoubleParam

     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

166. #### def summary: XGBoostTrainingSummary

     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.

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

     Definition Classes
       AnyRef

168. #### final val timeoutRequestWorkers: LongParam

     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

169. #### def toString(): String

     Definition Classes
       Identifiable → AnyRef → Any

170. #### final val trackerConf: TrackerConfParam

     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

171. #### final val trainTestRatio: DoubleParam

     Fraction of training points to use for testing.

     Fraction of training points to use for testing.

     Definition Classes
       LearningTaskParams

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

     Definition Classes
       XGBoostRegressionModel → PredictionModel → Transformer

173. #### def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

     Definition Classes
       Transformer
     Annotations
       @Since( "2.0.0" )

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

     Definition Classes
       Transformer
     Annotations
       @Since( "2.0.0" ) @varargs()

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

     Attributes
       protected
     Definition Classes
       PredictionModel

176. #### def transformSchema(schema: StructType): StructType

     Definition Classes
       PredictionModel → PipelineStage

177. #### def transformSchema(schema: StructType, logging: Boolean): StructType

     Attributes
       protected
     Definition Classes
       PipelineStage
     Annotations
       @DeveloperApi()

178. #### final val treeLimit: IntParam

     Definition Classes
       BoosterParams

179. #### final val treeMethod: Param[String]

     The tree construction algorithm used in XGBoost.

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

     Definition Classes
       BoosterParams

180. #### val uid: String

     Definition Classes
       XGBoostRegressionModel → Identifiable

181. #### final val useExternalMemory: BooleanParam

     whether to use external memory as cache.

     whether to use external memory as cache. default: false

     Definition Classes
       GeneralParams

182. #### def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

     Attributes
       protected
     Definition Classes
       PredictorParams

183. #### final val verbosity: IntParam

     Verbosity of printing messages.

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

     Definition Classes
       GeneralParams

184. #### final def wait(): Unit

     Definition Classes
       AnyRef
     Annotations
       @throws( ... )

185. #### final def wait(arg0: Long, arg1: Int): Unit

     Definition Classes
       AnyRef
     Annotations
       @throws( ... )

186. #### final def wait(arg0: Long): Unit

     Definition Classes
       AnyRef
     Annotations
       @throws( ... )

187. #### final val weightCol: Param[String]

     Definition Classes
       HasWeightCol

188. #### def write: MLWriter

     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 Identifiable

### Inherited from AnyRef

### Inherited from Any

### getParam

### param

### Ungrouped