Packages

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

Linear Supertypes
DefaultParamsWritable, MLWritable, XGBoostRegressorParams, NonParamVariables, 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. NonParamVariables
  6. HasContribPredictionCol
  7. HasLeafPredictionCol
  8. ParamMapFuncs
  9. HasGroupCol
  10. HasWeightCol
  11. HasBaseMarginCol
  12. LearningTaskParams
  13. BoosterParams
  14. GeneralParams
  15. Predictor
  16. PredictorParams
  17. HasPredictionCol
  18. HasFeaturesCol
  19. HasLabelCol
  20. Estimator
  21. PipelineStage
  22. Logging
  23. Params
  24. Serializable
  25. Serializable
  26. Identifiable
  27. AnyRef
  28. Any
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Visibility
  1. Public
  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 cacheTrainingSet: BooleanParam

    whether caching training data

    whether caching training data

    Definition Classes
    LearningTaskParams
  12. 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
  13. 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
  14. final def clear(param: Param[_]): XGBoostRegressor.this.type
    Definition Classes
    Params
  15. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  16. 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
  17. 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
  18. final val contribPredictionCol: Param[String]

    Param for contribution prediction column name.

    Param for contribution prediction column name.

    Definition Classes
    HasContribPredictionCol
  19. def copy(extra: ParamMap): XGBoostRegressor
    Definition Classes
    XGBoostRegressor → Predictor → Estimator → PipelineStage → Params
  20. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  21. final val customEval: CustomEvalParam

    customized evaluation function provided by user.

    customized evaluation function provided by user. default: null

    Definition Classes
    GeneralParams
  22. final val customObj: CustomObjParam

    customized objective function provided by user.

    customized objective function provided by user. default: null

    Definition Classes
    GeneralParams
  23. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  24. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  25. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  26. 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
  27. 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, rmsle, mae, logloss, error, merror, mlogloss, auc, aucpr, ndcg, map, gamma-deviance

    Definition Classes
    LearningTaskParams
  28. val evalSetsMap: Map[String, DataFrame]
    Attributes
    protected
    Definition Classes
    NonParamVariables
  29. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  30. def explainParams(): String
    Definition Classes
    Params
  31. def extractLabeledPoints(dataset: Dataset[_]): RDD[org.apache.spark.ml.feature.LabeledPoint]
    Attributes
    protected
    Definition Classes
    Predictor
  32. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  33. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  34. final val featuresCol: Param[String]
    Definition Classes
    HasFeaturesCol
  35. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  36. def fit(dataset: Dataset[_]): XGBoostRegressionModel
    Definition Classes
    Predictor → Estimator
  37. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[XGBoostRegressionModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  38. def fit(dataset: Dataset[_], paramMap: ParamMap): XGBoostRegressionModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  39. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): XGBoostRegressionModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  40. 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
  41. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  42. final def getAlpha: Double
    Definition Classes
    BoosterParams
  43. final def getBaseMarginCol: String

    Definition Classes
    HasBaseMarginCol
  44. final def getBaseScore: Double
    Definition Classes
    LearningTaskParams
  45. final def getCheckpointInterval: Int
    Definition Classes
    GeneralParams
  46. final def getCheckpointPath: String
    Definition Classes
    GeneralParams
  47. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  48. final def getColsampleBylevel: Double
    Definition Classes
    BoosterParams
  49. final def getColsampleBytree: Double
    Definition Classes
    BoosterParams
  50. final def getContribPredictionCol: String

    Definition Classes
    HasContribPredictionCol
  51. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  52. final def getEta: Double
    Definition Classes
    BoosterParams
  53. final def getEvalMetric: String
    Definition Classes
    LearningTaskParams
  54. def getEvalSets(params: Map[String, Any]): Map[String, DataFrame]
    Definition Classes
    NonParamVariables
  55. final def getFeaturesCol: String
    Definition Classes
    HasFeaturesCol
  56. final def getGamma: Double
    Definition Classes
    BoosterParams
  57. final def getGroupCol: String

    Definition Classes
    HasGroupCol
  58. final def getGrowPolicy: String
    Definition Classes
    BoosterParams
  59. final def getInteractionConstraints: String
    Definition Classes
    BoosterParams
  60. final def getLabelCol: String
    Definition Classes
    HasLabelCol
  61. final def getLambda: Double
    Definition Classes
    BoosterParams
  62. final def getLambdaBias: Double
    Definition Classes
    BoosterParams
  63. final def getLeafPredictionCol: String

    Definition Classes
    HasLeafPredictionCol
  64. final def getMaxBins: Int
    Definition Classes
    BoosterParams
  65. final def getMaxDeltaStep: Double
    Definition Classes
    BoosterParams
  66. final def getMaxDepth: Int
    Definition Classes
    BoosterParams
  67. final def getMaxLeaves: Int
    Definition Classes
    BoosterParams
  68. final def getMaximizeEvaluationMetrics: Boolean
    Definition Classes
    LearningTaskParams
  69. final def getMinChildWeight: Double
    Definition Classes
    BoosterParams
  70. final def getMissing: Float
    Definition Classes
    GeneralParams
  71. final def getMonotoneConstraints: String
    Definition Classes
    BoosterParams
  72. final def getNormalizeType: String
    Definition Classes
    BoosterParams
  73. final def getNthread: Int
    Definition Classes
    GeneralParams
  74. final def getNumEarlyStoppingRounds: Int
    Definition Classes
    LearningTaskParams
  75. final def getNumRound: Int
    Definition Classes
    GeneralParams
  76. final def getNumWorkers: Int
    Definition Classes
    GeneralParams
  77. final def getObjective: String
    Definition Classes
    LearningTaskParams
  78. final def getObjectiveType: String
    Definition Classes
    LearningTaskParams
  79. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  80. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  81. final def getPredictionCol: String
    Definition Classes
    HasPredictionCol
  82. final def getRateDrop: Double
    Definition Classes
    BoosterParams
  83. final def getSampleType: String
    Definition Classes
    BoosterParams
  84. final def getScalePosWeight: Double
    Definition Classes
    BoosterParams
  85. final def getSeed: Long
    Definition Classes
    GeneralParams
  86. final def getSilent: Int
    Definition Classes
    GeneralParams
  87. final def getSketchEps: Double
    Definition Classes
    BoosterParams
  88. final def getSkipDrop: Double
    Definition Classes
    BoosterParams
  89. final def getSubsample: Double
    Definition Classes
    BoosterParams
  90. final def getTimeoutRequestWorkers: Long
    Definition Classes
    GeneralParams
  91. final def getTrainTestRatio: Double
    Definition Classes
    LearningTaskParams
  92. final def getTreeLimit: Int
    Definition Classes
    BoosterParams
  93. final def getTreeMethod: String
    Definition Classes
    BoosterParams
  94. final def getUseExternalMemory: Boolean
    Definition Classes
    GeneralParams
  95. final def getVerbosity: Int
    Definition Classes
    GeneralParams
  96. final def getWeightCol: String
    Definition Classes
    HasWeightCol
  97. final val groupCol: Param[String]

    Param for group column name.

    Param for group column name.

    Definition Classes
    HasGroupCol
  98. final val growPolicy: Param[String]

    growth policy for fast histogram algorithm

    growth policy for fast histogram algorithm

    Definition Classes
    BoosterParams
  99. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  100. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  101. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  102. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  103. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  104. final val interactionConstraints: Param[String]
    Definition Classes
    BoosterParams
  105. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  106. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  107. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  108. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  109. final val labelCol: Param[String]
    Definition Classes
    HasLabelCol
  110. 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
  111. 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
  112. final val leafPredictionCol: Param[String]

    Param for leaf prediction column name.

    Param for leaf prediction column name.

    Definition Classes
    HasLeafPredictionCol
  113. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  114. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  115. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  116. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  117. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  118. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  119. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  120. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  121. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  122. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  123. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  124. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  125. final val maxBins: IntParam

    maximum number of bins in histogram

    maximum number of bins in histogram

    Definition Classes
    BoosterParams
  126. 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
  127. 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
  128. 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
  129. final val maximizeEvaluationMetrics: BooleanParam
    Definition Classes
    LearningTaskParams
  130. 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
  131. final val missing: FloatParam

    the value treated as missing.

    the value treated as missing. default: Float.NaN

    Definition Classes
    GeneralParams
  132. final val monotoneConstraints: Param[String]
    Definition Classes
    BoosterParams
  133. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  134. 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
  135. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  136. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  137. final val nthread: IntParam

    number of threads used by per worker.

    number of threads used by per worker. default 1

    Definition Classes
    GeneralParams
  138. 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
  139. final val numRound: IntParam

    The number of rounds for boosting

    The number of rounds for boosting

    Definition Classes
    GeneralParams
  140. 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
  141. 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:squarederror, reg:squaredlogerror, reg:logistic, binary:logistic, binary:logitraw, count:poisson, multi:softmax, multi:softprob, rank:pairwise, reg:gamma. default: reg:squarederror

    Definition Classes
    LearningTaskParams
  142. 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
  143. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  144. final val predictionCol: Param[String]
    Definition Classes
    HasPredictionCol
  145. final val rateDrop: DoubleParam

    Parameter of Dart booster.

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

    Definition Classes
    BoosterParams
  146. final val sampleType: Param[String]

    Parameter for Dart booster.

    Parameter for Dart booster. Type of sampling algorithm. "uniform": dropped trees are selected uniformly. "weighted": dropped trees are selected in proportion to weight. [default="uniform"]

    Definition Classes
    BoosterParams
  147. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  148. final val scalePosWeight: DoubleParam

    Control the balance of positive and negative weights, useful for unbalanced classes.

    Control the balance of positive and negative weights, useful for unbalanced classes. A typical value to consider: sum(negative cases) / sum(positive cases). [default=1]

    Definition Classes
    BoosterParams
  149. final val seed: LongParam

    Random seed for the C++ part of XGBoost and train/test splitting.

    Random seed for the C++ part of XGBoost and train/test splitting.

    Definition Classes
    GeneralParams
  150. final def set(paramPair: ParamPair[_]): XGBoostRegressor.this.type
    Attributes
    protected
    Definition Classes
    Params
  151. final def set(param: String, value: Any): XGBoostRegressor.this.type
    Attributes
    protected
    Definition Classes
    Params
  152. final def set[T](param: Param[T], value: T): XGBoostRegressor.this.type
    Definition Classes
    Params
  153. def setAlpha(value: Double): XGBoostRegressor.this.type
  154. def setBaseMarginCol(value: String): XGBoostRegressor.this.type
  155. def setBaseScore(value: Double): XGBoostRegressor.this.type
  156. def setCheckpointInterval(value: Int): XGBoostRegressor.this.type
  157. def setCheckpointPath(value: String): XGBoostRegressor.this.type
  158. def setColsampleBylevel(value: Double): XGBoostRegressor.this.type
  159. def setColsampleBytree(value: Double): XGBoostRegressor.this.type
  160. def setCustomEval(value: EvalTrait): XGBoostRegressor.this.type
  161. def setCustomObj(value: ObjectiveTrait): XGBoostRegressor.this.type
  162. final def setDefault(paramPairs: ParamPair[_]*): XGBoostRegressor.this.type
    Attributes
    protected
    Definition Classes
    Params
  163. final def setDefault[T](param: Param[T], value: T): XGBoostRegressor.this.type
    Attributes
    protected
    Definition Classes
    Params
  164. def setEta(value: Double): XGBoostRegressor.this.type
  165. def setEvalMetric(value: String): XGBoostRegressor.this.type
  166. def setEvalSets(evalSets: Map[String, DataFrame]): XGBoostRegressor.this.type
    Definition Classes
    NonParamVariables
  167. def setFeaturesCol(value: String): XGBoostRegressor
    Definition Classes
    Predictor
  168. def setGamma(value: Double): XGBoostRegressor.this.type
  169. def setGroupCol(value: String): XGBoostRegressor.this.type
  170. def setGrowPolicy(value: String): XGBoostRegressor.this.type
  171. def setLabelCol(value: String): XGBoostRegressor
    Definition Classes
    Predictor
  172. def setLambda(value: Double): XGBoostRegressor.this.type
  173. def setLambdaBias(value: Double): XGBoostRegressor.this.type
  174. def setMaxBins(value: Int): XGBoostRegressor.this.type
  175. def setMaxDeltaStep(value: Double): XGBoostRegressor.this.type
  176. def setMaxDepth(value: Int): XGBoostRegressor.this.type
  177. def setMaxLeaves(value: Int): XGBoostRegressor.this.type
  178. def setMaximizeEvaluationMetrics(value: Boolean): XGBoostRegressor.this.type
  179. def setMinChildWeight(value: Double): XGBoostRegressor.this.type
  180. def setMissing(value: Float): XGBoostRegressor.this.type
  181. def setNormalizeType(value: String): XGBoostRegressor.this.type
  182. def setNthread(value: Int): XGBoostRegressor.this.type
  183. def setNumEarlyStoppingRounds(value: Int): XGBoostRegressor.this.type
  184. def setNumRound(value: Int): XGBoostRegressor.this.type
  185. def setNumWorkers(value: Int): XGBoostRegressor.this.type
  186. def setObjective(value: String): XGBoostRegressor.this.type
  187. def setObjectiveType(value: String): XGBoostRegressor.this.type
  188. def setPredictionCol(value: String): XGBoostRegressor
    Definition Classes
    Predictor
  189. def setRateDrop(value: Double): XGBoostRegressor.this.type
  190. def setSampleType(value: String): XGBoostRegressor.this.type
  191. def setScalePosWeight(value: Double): XGBoostRegressor.this.type
  192. def setSeed(value: Long): XGBoostRegressor.this.type
  193. def setSilent(value: Int): XGBoostRegressor.this.type
  194. def setSketchEps(value: Double): XGBoostRegressor.this.type
  195. def setSkipDrop(value: Double): XGBoostRegressor.this.type
  196. def setSubsample(value: Double): XGBoostRegressor.this.type
  197. def setTimeoutRequestWorkers(value: Long): XGBoostRegressor.this.type
  198. def setTrainTestRatio(value: Double): XGBoostRegressor.this.type
  199. def setTreeMethod(value: String): XGBoostRegressor.this.type
  200. def setUseExternalMemory(value: Boolean): XGBoostRegressor.this.type
  201. def setWeightCol(value: String): XGBoostRegressor.this.type
  202. final val silent: IntParam

    Deprecated.

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

    Definition Classes
    GeneralParams
  203. 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
  204. final val skipCleanCheckpoint: BooleanParam

    whether cleaning checkpoint, always cleaning by default, having this parameter majorly for testing

    whether cleaning checkpoint, always cleaning by default, having this parameter majorly for testing

    Definition Classes
    LearningTaskParams
  205. 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
  206. 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
  207. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  208. 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
  209. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  210. 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
  211. def train(dataset: Dataset[_]): XGBoostRegressionModel
    Attributes
    protected
    Definition Classes
    XGBoostRegressor → Predictor
  212. final val trainTestRatio: DoubleParam

    Fraction of training points to use for testing.

    Fraction of training points to use for testing.

    Definition Classes
    LearningTaskParams
  213. def transformSchema(schema: StructType): StructType
    Definition Classes
    Predictor → PipelineStage
  214. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  215. final val treeLimit: IntParam
    Definition Classes
    BoosterParams
  216. 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
  217. val uid: String
    Definition Classes
    XGBoostRegressor → Identifiable
  218. final val useExternalMemory: BooleanParam

    whether to use external memory as cache.

    whether to use external memory as cache. default: false

    Definition Classes
    GeneralParams
  219. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
    Attributes
    protected
    Definition Classes
    PredictorParams
  220. 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
  221. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  222. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  223. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  224. final val weightCol: Param[String]
    Definition Classes
    HasWeightCol
  225. def write: MLWriter
    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from XGBoostRegressorParams

Inherited from NonParamVariables

Inherited from HasContribPredictionCol

Inherited from HasLeafPredictionCol

Inherited from ParamMapFuncs

Inherited from HasGroupCol

Inherited from HasWeightCol

Inherited from HasBaseMarginCol

Inherited from LearningTaskParams

Inherited from BoosterParams

Inherited from GeneralParams

Inherited from 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 Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

getParam

param

Ungrouped