ml.dmlc.xgboost4j.scala.spark

# XGBoostClassifier

###
Related Docs: object XGBoostClassifier | package spark

#### 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

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### Instance Constructors

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

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

3. #### new XGBoostClassifier()

4. #### new XGBoostClassifier(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[_]): XGBoostClassifier.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): XGBoostClassifier

    Definition Classes
      XGBoostClassifier → 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[_], numClasses: Int): RDD[org.apache.spark.ml.feature.LabeledPoint]

    Attributes
      protected
    Definition Classes
      Classifier

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

    Attributes
      protected
    Definition Classes
      Predictor

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

    Definition Classes
      Params

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

    Definition Classes
      Params

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

    Definition Classes
      HasFeaturesCol

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

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

35. #### def fit(dataset: Dataset[_]): XGBoostClassificationModel

    Definition Classes
      Predictor → Estimator

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

    Definition Classes
      Estimator
    Annotations
      @Since( "2.0.0" )

37. #### def fit(dataset: Dataset[_], paramMap: ParamMap): XGBoostClassificationModel

    Definition Classes
      Estimator
    Annotations
      @Since( "2.0.0" )

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

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

39. #### 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

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

    Definition Classes
      Params

41. #### final def getAlpha: Double

    Definition Classes
      BoosterParams

42. #### final def getBaseMarginCol: String

    Definition Classes
      HasBaseMarginCol

43. #### final def getBaseScore: Double

    Definition Classes
      LearningTaskParams

44. #### final def getCheckpointInterval: Int

    Definition Classes
      GeneralParams

45. #### final def getCheckpointPath: String

    Definition Classes
      GeneralParams

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

    Definition Classes
      AnyRef → Any

47. #### final def getColsampleBylevel: Double

    Definition Classes
      BoosterParams

48. #### final def getColsampleBytree: Double

    Definition Classes
      BoosterParams

49. #### final def getContribPredictionCol: String

    Definition Classes
      HasContribPredictionCol

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

    Definition Classes
      Params

51. #### final def getEta: Double

    Definition Classes
      BoosterParams

52. #### final def getEvalMetric: String

    Definition Classes
      LearningTaskParams

53. #### final def getFeaturesCol: String

    Definition Classes
      HasFeaturesCol

54. #### final def getGamma: Double

    Definition Classes
      BoosterParams

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 getNumClass: Int

    Definition Classes
      HasNumClass

68. #### def getNumClasses(dataset: Dataset[_], maxNumClasses: Int): Int

    Attributes
      protected
    Definition Classes
      Classifier

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 getOrDefault[T](param: Param[T]): T

    Definition Classes
      Params

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

    Definition Classes
      Params

75. #### final def getPredictionCol: String

    Definition Classes
      HasPredictionCol

76. #### final def getProbabilityCol: String

    Definition Classes
      HasProbabilityCol

77. #### final def getRateDrop: Double

    Definition Classes
      BoosterParams

78. #### final def getRawPredictionCol: String

    Definition Classes
      HasRawPredictionCol

79. #### final def getSampleType: String

    Definition Classes
      BoosterParams

80. #### final def getScalePosWeight: Double

    Definition Classes
      BoosterParams

81. #### final def getSeed: Long

    Definition Classes
      GeneralParams

82. #### final def getSilent: Int

    Definition Classes
      GeneralParams

83. #### final def getSketchEps: Double

    Definition Classes
      BoosterParams

84. #### final def getSkipDrop: Double

    Definition Classes
      BoosterParams

85. #### final def getSubsample: Double

    Definition Classes
      BoosterParams

86. #### def getThresholds: Array[Double]

    Definition Classes
      HasThresholds

87. #### final def getTimeoutRequestWorkers: Long

    Definition Classes
      GeneralParams

88. #### final def getTrainTestRatio: Double

    Definition Classes
      LearningTaskParams

89. #### final def getTreeLimit: Double

    Definition Classes
      BoosterParams

90. #### final def getTreeMethod: String

    Definition Classes
      BoosterParams

91. #### final def getUseExternalMemory: Boolean

    Definition Classes
      GeneralParams

92. #### final def getWeightCol: String

    Definition Classes
      HasWeightCol

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 hashCode(): Int

    Definition Classes
      AnyRef → Any

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

    Attributes
      protected
    Definition Classes
      Logging

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

    Attributes
      protected
    Definition Classes
      Logging

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

    Definition Classes
      Params

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

     Definition Classes
       Any

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

     Definition Classes
       Params

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Definition Classes
       HasLabelCol

104. #### 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

105. #### 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

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

     Param for leaf prediction column name.

     Param for leaf prediction column name.

     Definition Classes
       HasLeafPredictionCol

107. #### def log: Logger

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

114. #### def logName: String

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

119. #### final val maxBins: IntParam

     maximum number of bins in histogram

     maximum number of bins in histogram

     Definition Classes
       BoosterParams

120. #### 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

121. #### 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

122. #### 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

123. #### final val missing: FloatParam

     the value treated as missing.

     the value treated as missing. default: Float.NaN

     Definition Classes
       GeneralParams

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

     Definition Classes
       AnyRef

125. #### 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

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

     Definition Classes
       AnyRef

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

     Definition Classes
       AnyRef

128. #### final val nthread: IntParam

     number of threads used by per worker.

     number of threads used by per worker. default 1

     Definition Classes
       GeneralParams

129. #### final val numClass: IntParam

     number of classes

     number of classes

     Definition Classes
       HasNumClass

130. #### 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

131. #### final val numRound: IntParam

     The number of rounds for boosting

     The number of rounds for boosting

     Definition Classes
       GeneralParams

132. #### 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

133. #### 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

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

     Definition Classes
       Params

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

     Definition Classes
       HasPredictionCol

136. #### final val probabilityCol: Param[String]

     Definition Classes
       HasProbabilityCol

137. #### 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

138. #### final val rawPredictionCol: Param[String]

     Definition Classes
       HasRawPredictionCol

139. #### 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

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

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

141. #### 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

142. #### 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

143. #### final def set(paramPair: ParamPair[_]): XGBoostClassifier.this.type

     Attributes
       protected
     Definition Classes
       Params

144. #### final def set(param: String, value: Any): XGBoostClassifier.this.type

     Attributes
       protected
     Definition Classes
       Params

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

     Definition Classes
       Params

146. #### def setAlpha(value: Double): XGBoostClassifier.this.type

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

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

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

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

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

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

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

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

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

     Attributes
       protected
     Definition Classes
       Params

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

     Attributes
       protected
     Definition Classes
       Params

157. #### def setEta(value: Double): XGBoostClassifier.this.type

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

159. #### def setFeaturesCol(value: String): XGBoostClassifier

     Definition Classes
       Predictor

160. #### def setGamma(value: Double): XGBoostClassifier.this.type

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

162. #### def setLabelCol(value: String): XGBoostClassifier

     Definition Classes
       Predictor

163. #### def setLambda(value: Double): XGBoostClassifier.this.type

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

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

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

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

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

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

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

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

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

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

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

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

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

177. #### def setPredictionCol(value: String): XGBoostClassifier

     Definition Classes
       Predictor

178. #### def setProbabilityCol(value: String): XGBoostClassifier

     Definition Classes
       ProbabilisticClassifier

179. #### def setRateDrop(value: Double): XGBoostClassifier.this.type

180. #### def setRawPredictionCol(value: String): XGBoostClassifier

     Definition Classes
       Classifier

181. #### def setSampleType(value: String): XGBoostClassifier.this.type

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

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

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

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

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

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

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

     Definition Classes
       ProbabilisticClassifier

189. #### def setTimeoutRequestWorkers(value: Long): XGBoostClassifier.this.type

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

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

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

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

194. #### 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

195. #### 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

196. #### 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

197. #### 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

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

     Definition Classes
       AnyRef

199. #### final val thresholds: DoubleArrayParam

     Definition Classes
       HasThresholds

200. #### 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

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

     Definition Classes
       Identifiable → AnyRef → Any

202. #### 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

203. #### def train(dataset: Dataset[_]): XGBoostClassificationModel

     Attributes
       protected
     Definition Classes
       XGBoostClassifier → Predictor

204. #### final val trainTestRatio: DoubleParam

     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

     Definition Classes
       Predictor → PipelineStage

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

     Attributes
       protected
     Definition Classes
       PipelineStage
     Annotations
       @DeveloperApi()

207. #### final val treeLimit: IntParam

     Definition Classes
       BoosterParams

208. #### 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

209. #### val uid: String

     Definition Classes
       XGBoostClassifier → Identifiable

210. #### final val useExternalMemory: BooleanParam

     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

     Attributes
       protected
     Definition Classes
       ProbabilisticClassifierParams → ClassifierParams → PredictorParams

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

     Definition Classes
       AnyRef
     Annotations
       @throws( ... )

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

     Definition Classes
       AnyRef
     Annotations
       @throws( ... )

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

     Definition Classes
       AnyRef
     Annotations
       @throws( ... )

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

     Definition Classes
       HasWeightCol

216. #### def write: MLWriter

     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 Identifiable

### Inherited from AnyRef

### Inherited from Any

### getParam

### param

### Ungrouped