ml.dmlc.xgboost4j.scala.spark

# XGBoostRegressor

###
Related Docs: object XGBoostRegressor | package spark

#### class XGBoostRegressor extends Predictor[Vector, XGBoostRegressor, XGBoostRegressionModel] with XGBoostRegressorParams with DefaultParamsWritable

Linear Supertypes
DefaultParamsWritable, MLWritable, XGBoostRegressorParams, HasContribPredictionCol, HasLeafPredictionCol, ParamMapFuncs, HasGroupCol, HasWeightCol, HasBaseMarginCol, LearningTaskParams, BoosterParams, GeneralParams, Predictor[Vector, XGBoostRegressor, XGBoostRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Estimator[XGBoostRegressionModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited

1. XGBoostRegressor
2. DefaultParamsWritable
3. MLWritable
4. XGBoostRegressorParams
5. HasContribPredictionCol
6. HasLeafPredictionCol
7. ParamMapFuncs
8. HasGroupCol
9. HasWeightCol
10. HasBaseMarginCol
11. LearningTaskParams
12. BoosterParams
13. GeneralParams
14. Predictor
15. PredictorParams
16. HasPredictionCol
17. HasFeaturesCol
18. HasLabelCol
19. Estimator
20. PipelineStage
21. Logging
22. Params
23. Serializable
24. Serializable
25. Identifiable
26. AnyRef
27. Any

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2. All

### Instance Constructors

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

2. #### new XGBoostRegressor(uid: String)

3. #### new XGBoostRegressor()

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

### 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[_]): XGBoostRegressor.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): XGBoostRegressor

    Definition Classes
      XGBoostRegressor → Predictor → Estimator → 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. #### def explainParam(param: Param[_]): String

    Definition Classes
      Params

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

    Definition Classes
      Params

29. #### def extractLabeledPoints(dataset: Dataset[_]): RDD[org.apache.spark.ml.feature.LabeledPoint]

    Attributes
      protected
    Definition Classes
      Predictor

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 finalize(): Unit

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

34. #### def fit(dataset: Dataset[_]): XGBoostRegressionModel

    Definition Classes
      Predictor → Estimator

35. #### def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[XGBoostRegressionModel]

    Definition Classes
      Estimator
    Annotations
      @Since( "2.0.0" )

36. #### def fit(dataset: Dataset[_], paramMap: ParamMap): XGBoostRegressionModel

    Definition Classes
      Estimator
    Annotations
      @Since( "2.0.0" )

37. #### def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): XGBoostRegressionModel

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

38. #### 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

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

    Definition Classes
      Params

40. #### final def getAlpha: Double

    Definition Classes
      BoosterParams

41. #### final def getBaseMarginCol: String

    Definition Classes
      HasBaseMarginCol

42. #### final def getBaseScore: Double

    Definition Classes
      LearningTaskParams

43. #### final def getCheckpointInterval: Int

    Definition Classes
      GeneralParams

44. #### final def getCheckpointPath: String

    Definition Classes
      GeneralParams

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

    Definition Classes
      AnyRef → Any

46. #### final def getColsampleBylevel: Double

    Definition Classes
      BoosterParams

47. #### final def getColsampleBytree: Double

    Definition Classes
      BoosterParams

48. #### final def getContribPredictionCol: String

    Definition Classes
      HasContribPredictionCol

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

    Definition Classes
      Params

50. #### final def getEta: Double

    Definition Classes
      BoosterParams

51. #### final def getEvalMetric: String

    Definition Classes
      LearningTaskParams

52. #### final def getFeaturesCol: String

    Definition Classes
      HasFeaturesCol

53. #### final def getGamma: Double

    Definition Classes
      BoosterParams

54. #### final def getGroupCol: String

    Definition Classes
      HasGroupCol

55. #### final def getGrowPolicy: String

    Definition Classes
      BoosterParams

56. #### final def getLabelCol: String

    Definition Classes
      HasLabelCol

57. #### final def getLambda: Double

    Definition Classes
      BoosterParams

58. #### final def getLambdaBias: Double

    Definition Classes
      BoosterParams

59. #### final def getLeafPredictionCol: String

    Definition Classes
      HasLeafPredictionCol

60. #### final def getMaxBins: Int

    Definition Classes
      BoosterParams

61. #### final def getMaxDeltaStep: Double

    Definition Classes
      BoosterParams

62. #### final def getMaxDepth: Int

    Definition Classes
      BoosterParams

63. #### final def getMinChildWeight: Double

    Definition Classes
      BoosterParams

64. #### final def getMissing: Float

    Definition Classes
      GeneralParams

65. #### final def getNormalizeType: String

    Definition Classes
      BoosterParams

66. #### final def getNthread: Int

    Definition Classes
      GeneralParams

67. #### final def getNumEarlyStoppingRounds: Int

    Definition Classes
      LearningTaskParams

68. #### final def getNumRound: Int

    Definition Classes
      GeneralParams

69. #### final def getNumWorkers: Int

    Definition Classes
      GeneralParams

70. #### final def getObjective: String

    Definition Classes
      LearningTaskParams

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

    Definition Classes
      Params

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

    Definition Classes
      Params

73. #### final def getPredictionCol: String

    Definition Classes
      HasPredictionCol

74. #### final def getRateDrop: Double

    Definition Classes
      BoosterParams

75. #### final def getSampleType: String

    Definition Classes
      BoosterParams

76. #### final def getScalePosWeight: Double

    Definition Classes
      BoosterParams

77. #### final def getSeed: Long

    Definition Classes
      GeneralParams

78. #### final def getSilent: Int

    Definition Classes
      GeneralParams

79. #### final def getSketchEps: Double

    Definition Classes
      BoosterParams

80. #### final def getSkipDrop: Double

    Definition Classes
      BoosterParams

81. #### final def getSubsample: Double

    Definition Classes
      BoosterParams

82. #### final def getTimeoutRequestWorkers: Long

    Definition Classes
      GeneralParams

83. #### final def getTrainTestRatio: Double

    Definition Classes
      LearningTaskParams

84. #### final def getTreeLimit: Double

    Definition Classes
      BoosterParams

85. #### final def getTreeMethod: String

    Definition Classes
      BoosterParams

86. #### final def getUseExternalMemory: Boolean

    Definition Classes
      GeneralParams

87. #### final def getWeightCol: String

    Definition Classes
      HasWeightCol

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

    Param for group column name.

    Param for group column name.

    Definition Classes
      HasGroupCol

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

    growth policy for fast histogram algorithm

    growth policy for fast histogram algorithm

    Definition Classes
      BoosterParams

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

    Definition Classes
      Params

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

    Definition Classes
      Params

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

    Definition Classes
      AnyRef → Any

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

    Attributes
      protected
    Definition Classes
      Logging

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

    Attributes
      protected
    Definition Classes
      Logging

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

    Definition Classes
      Params

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

    Definition Classes
      Any

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

    Definition Classes
      Params

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

    Attributes
      protected
    Definition Classes
      Logging

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

    Definition Classes
      HasLabelCol

100. #### 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

101. #### 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

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

     Param for leaf prediction column name.

     Param for leaf prediction column name.

     Definition Classes
       HasLeafPredictionCol

103. #### def log: Logger

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

110. #### def logName: String

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

115. #### final val maxBins: IntParam

     maximum number of bins in histogram

     maximum number of bins in histogram

     Definition Classes
       BoosterParams

116. #### 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

117. #### 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

118. #### 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

119. #### final val missing: FloatParam

     the value treated as missing.

     the value treated as missing. default: Float.NaN

     Definition Classes
       GeneralParams

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

     Definition Classes
       AnyRef

121. #### 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

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

     Definition Classes
       AnyRef

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

     Definition Classes
       AnyRef

124. #### final val nthread: IntParam

     number of threads used by per worker.

     number of threads used by per worker. default 1

     Definition Classes
       GeneralParams

125. #### 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

126. #### final val numRound: IntParam

     The number of rounds for boosting

     The number of rounds for boosting

     Definition Classes
       GeneralParams

127. #### 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

128. #### 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

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

     Definition Classes
       Params

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

     Definition Classes
       HasPredictionCol

131. #### 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

132. #### 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

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

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

134. #### 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

135. #### 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

136. #### final def set(paramPair: ParamPair[_]): XGBoostRegressor.this.type

     Attributes
       protected
     Definition Classes
       Params

137. #### final def set(param: String, value: Any): XGBoostRegressor.this.type

     Attributes
       protected
     Definition Classes
       Params

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

     Definition Classes
       Params

139. #### def setAlpha(value: Double): XGBoostRegressor.this.type

140. #### def setBaseMarginCol(value: String): XGBoostRegressor.this.type

141. #### def setBaseScore(value: Double): XGBoostRegressor.this.type

142. #### def setCheckpointInterval(value: Int): XGBoostRegressor.this.type

143. #### def setCheckpointPath(value: String): XGBoostRegressor.this.type

144. #### def setColsampleBylevel(value: Double): XGBoostRegressor.this.type

145. #### def setColsampleBytree(value: Double): XGBoostRegressor.this.type

146. #### def setCustomEval(value: EvalTrait): XGBoostRegressor.this.type

147. #### def setCustomObj(value: ObjectiveTrait): XGBoostRegressor.this.type

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

     Attributes
       protected
     Definition Classes
       Params

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

     Attributes
       protected
     Definition Classes
       Params

150. #### def setEta(value: Double): XGBoostRegressor.this.type

151. #### def setEvalMetric(value: String): XGBoostRegressor.this.type

152. #### def setFeaturesCol(value: String): XGBoostRegressor

     Definition Classes
       Predictor

153. #### def setGamma(value: Double): XGBoostRegressor.this.type

154. #### def setGroupCol(value: String): XGBoostRegressor.this.type

155. #### def setGrowPolicy(value: String): XGBoostRegressor.this.type

156. #### def setLabelCol(value: String): XGBoostRegressor

     Definition Classes
       Predictor

157. #### def setLambda(value: Double): XGBoostRegressor.this.type

158. #### def setLambdaBias(value: Double): XGBoostRegressor.this.type

159. #### def setMaxBins(value: Int): XGBoostRegressor.this.type

160. #### def setMaxDeltaStep(value: Double): XGBoostRegressor.this.type

161. #### def setMaxDepth(value: Int): XGBoostRegressor.this.type

162. #### def setMinChildWeight(value: Double): XGBoostRegressor.this.type

163. #### def setMissing(value: Float): XGBoostRegressor.this.type

164. #### def setNormalizeType(value: String): XGBoostRegressor.this.type

165. #### def setNthread(value: Int): XGBoostRegressor.this.type

166. #### def setNumEarlyStoppingRounds(value: Int): XGBoostRegressor.this.type

167. #### def setNumRound(value: Int): XGBoostRegressor.this.type

168. #### def setNumWorkers(value: Int): XGBoostRegressor.this.type

169. #### def setObjective(value: String): XGBoostRegressor.this.type

170. #### def setPredictionCol(value: String): XGBoostRegressor

     Definition Classes
       Predictor

171. #### def setRateDrop(value: Double): XGBoostRegressor.this.type

172. #### def setSampleType(value: String): XGBoostRegressor.this.type

173. #### def setScalePosWeight(value: Double): XGBoostRegressor.this.type

174. #### def setSeed(value: Long): XGBoostRegressor.this.type

175. #### def setSilent(value: Int): XGBoostRegressor.this.type

176. #### def setSketchEps(value: Double): XGBoostRegressor.this.type

177. #### def setSkipDrop(value: Double): XGBoostRegressor.this.type

178. #### def setSubsample(value: Double): XGBoostRegressor.this.type

179. #### def setTimeoutRequestWorkers(value: Long): XGBoostRegressor.this.type

180. #### def setTrainTestRatio(value: Double): XGBoostRegressor.this.type

181. #### def setTreeMethod(value: String): XGBoostRegressor.this.type

182. #### def setUseExternalMemory(value: Boolean): XGBoostRegressor.this.type

183. #### def setWeightCol(value: String): XGBoostRegressor.this.type

184. #### final val silent: IntParam

     0 means printing running messages, 1 means silent mode.

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

     Definition Classes
       GeneralParams

185. #### 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

186. #### 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

187. #### 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

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

     Definition Classes
       AnyRef

189. #### 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

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

     Definition Classes
       Identifiable → AnyRef → Any

191. #### 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

192. #### def train(dataset: Dataset[_]): XGBoostRegressionModel

     Attributes
       protected
     Definition Classes
       XGBoostRegressor → Predictor

193. #### final val trainTestRatio: DoubleParam

     Fraction of training points to use for testing.

     Fraction of training points to use for testing.

     Definition Classes
       LearningTaskParams

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

     Definition Classes
       Predictor → PipelineStage

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

     Attributes
       protected
     Definition Classes
       PipelineStage
     Annotations
       @DeveloperApi()

196. #### final val treeLimit: IntParam

     Definition Classes
       BoosterParams

197. #### 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

198. #### val uid: String

     Definition Classes
       XGBoostRegressor → Identifiable

199. #### final val useExternalMemory: BooleanParam

     whether to use external memory as cache.

     whether to use external memory as cache. default: false

     Definition Classes
       GeneralParams

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

     Attributes
       protected
     Definition Classes
       PredictorParams

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

     Definition Classes
       AnyRef
     Annotations
       @throws( ... )

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

     Definition Classes
       AnyRef
     Annotations
       @throws( ... )

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

     Definition Classes
       AnyRef
     Annotations
       @throws( ... )

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

     Definition Classes
       HasWeightCol

205. #### def write: MLWriter

     Definition Classes
       DefaultParamsWritable → MLWritable

### Inherited from DefaultParamsWritable

### Inherited from MLWritable

### Inherited from XGBoostRegressorParams

### 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 Predictor[Vector, XGBoostRegressor, XGBoostRegressionModel]

### Inherited from PredictorParams

### Inherited from HasPredictionCol

### Inherited from HasFeaturesCol

### Inherited from HasLabelCol

### Inherited from Estimator[XGBoostRegressionModel]

### Inherited from PipelineStage

### Inherited from Logging

### Inherited from Params

### Inherited from Serializable

### Inherited from Identifiable

### Inherited from AnyRef

### Inherited from Any

### getParam

### param

### Ungrouped