ml.dmlc.xgboost4j.scala.spark

# XGBoostClassificationModel

###
Related Docs: object XGBoostClassificationModel | package spark

#### class XGBoostClassificationModel extends ProbabilisticClassificationModel[Vector, XGBoostClassificationModel] with XGBoostClassifierParams with MLWritable with Serializable

Linear Supertypes
MLWritable, XGBoostClassifierParams, NonParamVariables, HasContribPredictionCol, HasLeafPredictionCol, ParamMapFuncs, HasNumClass, HasBaseMarginCol, HasWeightCol, BoosterParams, LearningTaskParams, GeneralParams, ProbabilisticClassificationModel[Vector, XGBoostClassificationModel], ProbabilisticClassifierParams, HasThresholds, HasProbabilityCol, ClassificationModel[Vector, XGBoostClassificationModel], ClassifierParams, HasRawPredictionCol, PredictionModel[Vector, XGBoostClassificationModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Model[XGBoostClassificationModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited

1. XGBoostClassificationModel
2. MLWritable
3. XGBoostClassifierParams
4. NonParamVariables
5. HasContribPredictionCol
6. HasLeafPredictionCol
7. ParamMapFuncs
8. HasNumClass
9. HasBaseMarginCol
10. HasWeightCol
11. BoosterParams
12. LearningTaskParams
13. GeneralParams
14. ProbabilisticClassificationModel
15. ProbabilisticClassifierParams
16. HasThresholds
17. HasProbabilityCol
18. ClassificationModel
19. ClassifierParams
20. HasRawPredictionCol
21. PredictionModel
22. PredictorParams
23. HasPredictionCol
24. HasFeaturesCol
25. HasLabelCol
26. Model
27. Transformer
28. PipelineStage
29. Logging
30. Params
31. Serializable
32. Serializable
33. Identifiable
34. AnyRef
35. Any

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### Instance Constructors

1. #### new XGBoostClassificationModel(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[_]): XGBoostClassificationModel.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): XGBoostClassificationModel

    Definition Classes
      XGBoostClassificationModel → 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 getGrowPolicy: String

    Definition Classes
      BoosterParams

53. #### final def getInteractionConstraints: String

    Definition Classes
      BoosterParams

54. #### final def getLabelCol: String

    Definition Classes
      HasLabelCol

55. #### final def getLambda: Double

    Definition Classes
      BoosterParams

56. #### final def getLambdaBias: Double

    Definition Classes
      BoosterParams

57. #### final def getLeafPredictionCol: String

    Definition Classes
      HasLeafPredictionCol

58. #### final def getMaxBins: Int

    Definition Classes
      BoosterParams

59. #### final def getMaxDeltaStep: Double

    Definition Classes
      BoosterParams

60. #### final def getMaxDepth: Int

    Definition Classes
      BoosterParams

61. #### final def getMaxLeaves: Int

    Definition Classes
      BoosterParams

62. #### final def getMaximizeEvaluationMetrics: Boolean

    Definition Classes
      LearningTaskParams

63. #### final def getMinChildWeight: Double

    Definition Classes
      BoosterParams

64. #### final def getMissing: Float

    Definition Classes
      GeneralParams

65. #### final def getMonotoneConstraints: String

    Definition Classes
      BoosterParams

66. #### final def getNormalizeType: String

    Definition Classes
      BoosterParams

67. #### final def getNthread: Int

    Definition Classes
      GeneralParams

68. #### final def getNumClass: Int

    Definition Classes
      HasNumClass

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 getProbabilityCol: String

    Definition Classes
      HasProbabilityCol

78. #### final def getRateDrop: Double

    Definition Classes
      BoosterParams

79. #### final def getRawPredictionCol: String

    Definition Classes
      HasRawPredictionCol

80. #### final def getSampleType: String

    Definition Classes
      BoosterParams

81. #### final def getScalePosWeight: Double

    Definition Classes
      BoosterParams

82. #### final def getSeed: Long

    Definition Classes
      GeneralParams

83. #### final def getSilent: Int

    Definition Classes
      GeneralParams

84. #### final def getSketchEps: Double

    Definition Classes
      BoosterParams

85. #### final def getSkipDrop: Double

    Definition Classes
      BoosterParams

86. #### final def getSubsample: Double

    Definition Classes
      BoosterParams

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

    Definition Classes
      HasThresholds

88. #### final def getTimeoutRequestWorkers: Long

    Definition Classes
      GeneralParams

89. #### final def getTrainTestRatio: Double

    Definition Classes
      LearningTaskParams

90. #### final def getTreeLimit: Int

    Definition Classes
      BoosterParams

91. #### final def getTreeMethod: String

    Definition Classes
      BoosterParams

92. #### final def getUseExternalMemory: Boolean

    Definition Classes
      GeneralParams

93. #### final def getVerbosity: Int

    Definition Classes
      GeneralParams

94. #### final def getWeightCol: String

    Definition Classes
      HasWeightCol

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

    growth policy for fast histogram algorithm

    growth policy for fast histogram algorithm

    Definition Classes
      BoosterParams

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

    Definition Classes
      Params

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

    Definition Classes
      Params

98. #### def hasParent: Boolean

    Definition Classes
      Model

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

    Definition Classes
      AnyRef → Any

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Definition Classes
       BoosterParams

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

     Definition Classes
       Params

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

     Definition Classes
       Any

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

     Definition Classes
       Params

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Definition Classes
       HasLabelCol

108. #### 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

109. #### 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

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

     Param for leaf prediction column name.

     Param for leaf prediction column name.

     Definition Classes
       HasLeafPredictionCol

111. #### def log: Logger

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

118. #### def logName: String

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

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

     Attributes
       protected
     Definition Classes
       Logging

123. #### final val maxBins: IntParam

     maximum number of bins in histogram

     maximum number of bins in histogram

     Definition Classes
       BoosterParams

124. #### 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

125. #### 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

126. #### 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

127. #### final val maximizeEvaluationMetrics: BooleanParam

     Definition Classes
       LearningTaskParams

128. #### 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

129. #### final val missing: FloatParam

     the value treated as missing.

     the value treated as missing. default: Float.NaN

     Definition Classes
       GeneralParams

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

     Definition Classes
       BoosterParams

131. #### 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".

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

     Definition Classes
       AnyRef

133. #### 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

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

     Definition Classes
       AnyRef

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

     Definition Classes
       AnyRef

136. #### final val nthread: IntParam

     number of threads used by per worker.

     number of threads used by per worker. default 1

     Definition Classes
       GeneralParams

137. #### final val numClass: IntParam

     number of classes

     number of classes

     Definition Classes
       HasNumClass

138. #### val numClasses: Int

     Definition Classes
       XGBoostClassificationModel → ClassificationModel

139. #### 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

140. #### def numFeatures: Int

     Definition Classes
       PredictionModel
     Annotations
       @Since( "1.6.0" )

141. #### final val numRound: IntParam

     The number of rounds for boosting

     The number of rounds for boosting

     Definition Classes
       GeneralParams

142. #### 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

143. #### 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

144. #### 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

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

     Definition Classes
       Params

146. #### var parent: Estimator[XGBoostClassificationModel]

     Definition Classes
       Model

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

     Single instance prediction.

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

     Definition Classes
       XGBoostClassificationModel → ClassificationModel → PredictionModel

148. #### def predictProbability(features: Vector): Vector

     Attributes
       protected
     Definition Classes
       ProbabilisticClassificationModel

149. #### def predictRaw(features: Vector): Vector

     Attributes
       protected
     Definition Classes
       XGBoostClassificationModel → ClassificationModel

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

     Definition Classes
       HasPredictionCol

151. #### def probability2prediction(probability: Vector): Double

     Attributes
       protected
     Definition Classes
       ProbabilisticClassificationModel

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

     Definition Classes
       HasProbabilityCol

153. #### 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

154. #### def raw2prediction(rawPrediction: Vector): Double

     Attributes
       protected
     Definition Classes
       ProbabilisticClassificationModel → ClassificationModel

155. #### def raw2probability(rawPrediction: Vector): Vector

     Attributes
       protected
     Definition Classes
       ProbabilisticClassificationModel

156. #### def raw2probabilityInPlace(rawPrediction: Vector): Vector

     Attributes
       protected
     Definition Classes
       XGBoostClassificationModel → ProbabilisticClassificationModel

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

     Definition Classes
       HasRawPredictionCol

158. #### 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

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

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

160. #### 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

161. #### 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

162. #### final def set(paramPair: ParamPair[_]): XGBoostClassificationModel.this.type

     Attributes
       protected
     Definition Classes
       Params

163. #### final def set(param: String, value: Any): XGBoostClassificationModel.this.type

     Attributes
       protected
     Definition Classes
       Params

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

     Definition Classes
       Params

165. #### def setContribPredictionCol(value: String): XGBoostClassificationModel.this.type

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

     Attributes
       protected
     Definition Classes
       Params

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

     Attributes
       protected
     Definition Classes
       Params

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

     Definition Classes
       NonParamVariables

169. #### def setFeaturesCol(value: String): XGBoostClassificationModel

     Definition Classes
       PredictionModel

170. #### def setLeafPredictionCol(value: String): XGBoostClassificationModel.this.type

171. #### def setParent(parent: Estimator[XGBoostClassificationModel]): XGBoostClassificationModel

     Definition Classes
       Model

172. #### def setPredictionCol(value: String): XGBoostClassificationModel

     Definition Classes
       PredictionModel

173. #### def setProbabilityCol(value: String): XGBoostClassificationModel

     Definition Classes
       ProbabilisticClassificationModel

174. #### def setRawPredictionCol(value: String): XGBoostClassificationModel

     Definition Classes
       ClassificationModel

175. #### def setThresholds(value: Array[Double]): XGBoostClassificationModel

     Definition Classes
       ProbabilisticClassificationModel

176. #### def setTreeLimit(value: Int): XGBoostClassificationModel.this.type

177. #### final val silent: IntParam

     Deprecated.

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

     Definition Classes
       GeneralParams

178. #### 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

179. #### 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

180. #### 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

181. #### 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.

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

     Definition Classes
       AnyRef

183. #### final val thresholds: DoubleArrayParam

     Definition Classes
       HasThresholds

184. #### 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

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

     Definition Classes
       Identifiable → AnyRef → Any

186. #### 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

187. #### final val trainTestRatio: DoubleParam

     Fraction of training points to use for testing.

     Fraction of training points to use for testing.

     Definition Classes
       LearningTaskParams

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

     Definition Classes
       XGBoostClassificationModel → ProbabilisticClassificationModel → ClassificationModel → PredictionModel → Transformer

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

     Definition Classes
       Transformer
     Annotations
       @Since( "2.0.0" )

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

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

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

     Attributes
       protected
     Definition Classes
       PredictionModel

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

     Definition Classes
       PredictionModel → PipelineStage

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

     Attributes
       protected
     Definition Classes
       PipelineStage
     Annotations
       @DeveloperApi()

194. #### final val treeLimit: IntParam

     Definition Classes
       BoosterParams

195. #### 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

196. #### val uid: String

     Definition Classes
       XGBoostClassificationModel → Identifiable

197. #### final val useExternalMemory: BooleanParam

     whether to use external memory as cache.

     whether to use external memory as cache. default: false

     Definition Classes
       GeneralParams

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

     Attributes
       protected
     Definition Classes
       ProbabilisticClassifierParams → ClassifierParams → PredictorParams

199. #### 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

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

     Definition Classes
       AnyRef
     Annotations
       @throws( ... )

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

     Definition Classes
       AnyRef
     Annotations
       @throws( ... )

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

     Definition Classes
       AnyRef
     Annotations
       @throws( ... )

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

     Definition Classes
       HasWeightCol

204. #### def write: MLWriter

     Definition Classes
       XGBoostClassificationModel → MLWritable

### Inherited from MLWritable

### Inherited from XGBoostClassifierParams

### Inherited from NonParamVariables

### 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 ProbabilisticClassificationModel[Vector, XGBoostClassificationModel]

### Inherited from ProbabilisticClassifierParams

### Inherited from HasThresholds

### Inherited from HasProbabilityCol

### Inherited from ClassificationModel[Vector, XGBoostClassificationModel]

### Inherited from ClassifierParams

### Inherited from HasRawPredictionCol

### Inherited from PredictionModel[Vector, XGBoostClassificationModel]

### Inherited from PredictorParams

### Inherited from HasPredictionCol

### Inherited from HasFeaturesCol

### Inherited from HasLabelCol

### Inherited from Model[XGBoostClassificationModel]

### 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