Packages

class XGBoostRegressionModel extends PredictionModel[Vector, XGBoostRegressionModel] with XGBoostRegressorParams with InferenceParams with MLWritable with Serializable

Linear Supertypes
MLWritable, InferenceParams, XGBoostRegressorParams, NonParamVariables, HasContribPredictionCol, HasLeafPredictionCol, ParamMapFuncs, HasGroupCol, HasWeightCol, HasBaseMarginCol, LearningTaskParams, BoosterParams, GeneralParams, PredictionModel[Vector, XGBoostRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Model[XGBoostRegressionModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. XGBoostRegressionModel
  2. MLWritable
  3. InferenceParams
  4. XGBoostRegressorParams
  5. NonParamVariables
  6. HasContribPredictionCol
  7. HasLeafPredictionCol
  8. ParamMapFuncs
  9. HasGroupCol
  10. HasWeightCol
  11. HasBaseMarginCol
  12. LearningTaskParams
  13. BoosterParams
  14. GeneralParams
  15. PredictionModel
  16. PredictorParams
  17. HasPredictionCol
  18. HasFeaturesCol
  19. HasLabelCol
  20. Model
  21. Transformer
  22. PipelineStage
  23. Logging
  24. Params
  25. Serializable
  26. Serializable
  27. Identifiable
  28. AnyRef
  29. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new XGBoostRegressionModel(uid: String)

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T
    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. def MLlib2XGBoostParams: Map[String, Any]
    Definition Classes
    ParamMapFuncs
  6. def XGBoostToMLlibParams(xgboostParams: Map[String, Any]): Unit
    Definition Classes
    ParamMapFuncs
  7. final val alpha: DoubleParam

    L1 regularization term on weights, increase this value will make model more conservative.

    L1 regularization term on weights, increase this value will make model more conservative. [default=0]

    Definition Classes
    BoosterParams
  8. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  9. final val baseMarginCol: Param[String]

    Param for initial prediction (aka base margin) column name.

    Param for initial prediction (aka base margin) column name.

    Definition Classes
    HasBaseMarginCol
  10. final val baseScore: DoubleParam

    the initial prediction score of all instances, global bias.

    the initial prediction score of all instances, global bias. default=0.5

    Definition Classes
    LearningTaskParams
  11. final val 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[_]): XGBoostRegressionModel.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): XGBoostRegressionModel
    Definition Classes
    XGBoostRegressionModel → Model → Transformer → 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. 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 featuresDataType: DataType
    Attributes
    protected
    Definition Classes
    PredictionModel
  35. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  36. 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
  37. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  38. final def getAlpha: Double
    Definition Classes
    BoosterParams
  39. final def getBaseMarginCol: String

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

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

    Definition Classes
    HasGroupCol
  54. final def getGrowPolicy: String
    Definition Classes
    BoosterParams
  55. final def getInferBatchSize: Int

    Definition Classes
    InferenceParams
  56. final def getInteractionConstraints: String
    Definition Classes
    BoosterParams
  57. final def getLabelCol: String
    Definition Classes
    HasLabelCol
  58. final def getLambda: Double
    Definition Classes
    BoosterParams
  59. final def getLambdaBias: Double
    Definition Classes
    BoosterParams
  60. final def getLeafPredictionCol: String

    Definition Classes
    HasLeafPredictionCol
  61. final def getMaxBins: Int
    Definition Classes
    BoosterParams
  62. final def getMaxDeltaStep: Double
    Definition Classes
    BoosterParams
  63. final def getMaxDepth: Int
    Definition Classes
    BoosterParams
  64. final def getMaxLeaves: Int
    Definition Classes
    BoosterParams
  65. final def getMaximizeEvaluationMetrics: Boolean
    Definition Classes
    LearningTaskParams
  66. final def getMinChildWeight: Double
    Definition Classes
    BoosterParams
  67. final def getMissing: Float
    Definition Classes
    GeneralParams
  68. final def getMonotoneConstraints: String
    Definition Classes
    BoosterParams
  69. final def getNormalizeType: String
    Definition Classes
    BoosterParams
  70. final def getNthread: Int
    Definition Classes
    GeneralParams
  71. final def getNumEarlyStoppingRounds: Int
    Definition Classes
    LearningTaskParams
  72. final def getNumRound: Int
    Definition Classes
    GeneralParams
  73. final def getNumWorkers: Int
    Definition Classes
    GeneralParams
  74. final def getObjective: String
    Definition Classes
    LearningTaskParams
  75. final def getObjectiveType: String
    Definition Classes
    LearningTaskParams
  76. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  77. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  78. final def getPredictionCol: String
    Definition Classes
    HasPredictionCol
  79. final def getRateDrop: Double
    Definition Classes
    BoosterParams
  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. final def getTimeoutRequestWorkers: Long
    Definition Classes
    GeneralParams
  88. final def getTrainTestRatio: Double
    Definition Classes
    LearningTaskParams
  89. final def getTreeLimit: Int
    Definition Classes
    BoosterParams
  90. final def getTreeMethod: String
    Definition Classes
    BoosterParams
  91. final def getUseExternalMemory: Boolean
    Definition Classes
    GeneralParams
  92. final def getVerbosity: Int
    Definition Classes
    GeneralParams
  93. final def getWeightCol: String
    Definition Classes
    HasWeightCol
  94. final val groupCol: Param[String]

    Param for group column name.

    Param for group column name.

    Definition Classes
    HasGroupCol
  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
    Annotations
    @native()
  100. final val inferBatchSize: IntParam

    batch size of inference iteration

    batch size of inference iteration

    Definition Classes
    InferenceParams
  101. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  102. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  103. final val interactionConstraints: Param[String]
    Definition Classes
    BoosterParams
  104. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  105. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  106. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  107. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  108. final val labelCol: Param[String]
    Definition Classes
    HasLabelCol
  109. 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
  110. 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
  111. final val leafPredictionCol: Param[String]

    Param for leaf prediction column name.

    Param for leaf prediction column name.

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

    maximum number of bins in histogram

    maximum number of bins in histogram

    Definition Classes
    BoosterParams
  125. 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
  126. 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
  127. 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
  128. final val maximizeEvaluationMetrics: BooleanParam
    Definition Classes
    LearningTaskParams
  129. 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
  130. final val missing: FloatParam

    the value treated as missing.

    the value treated as missing. default: Float.NaN

    Definition Classes
    GeneralParams
  131. final val monotoneConstraints: Param[String]
    Definition Classes
    BoosterParams
  132. 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".

  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. def numFeatures: Int
    Definition Classes
    PredictionModel
    Annotations
    @Since( "1.6.0" )
  140. final val numRound: IntParam

    The number of rounds for boosting

    The number of rounds for boosting

    Definition Classes
    GeneralParams
  141. 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
  142. 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
  143. 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
  144. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  145. var parent: Estimator[XGBoostRegressionModel]
    Definition Classes
    Model
  146. def predict(features: Vector): Double

    Single instance prediction.

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

    Definition Classes
    XGBoostRegressionModel → PredictionModel
  147. final val predictionCol: Param[String]
    Definition Classes
    HasPredictionCol
  148. 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
  149. 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
  150. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  151. 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
  152. 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
  153. final def set(paramPair: ParamPair[_]): XGBoostRegressionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  154. final def set(param: String, value: Any): XGBoostRegressionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  155. final def set[T](param: Param[T], value: T): XGBoostRegressionModel.this.type
    Definition Classes
    Params
  156. def setContribPredictionCol(value: String): XGBoostRegressionModel.this.type
  157. final def setDefault(paramPairs: ParamPair[_]*): XGBoostRegressionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  158. final def setDefault[T](param: Param[T], value: T): XGBoostRegressionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  159. def setEvalSets(evalSets: Map[String, DataFrame]): XGBoostRegressionModel.this.type
    Definition Classes
    NonParamVariables
  160. def setFeaturesCol(value: String): XGBoostRegressionModel
    Definition Classes
    PredictionModel
  161. def setInferBatchSize(value: Int): XGBoostRegressionModel.this.type
  162. def setLeafPredictionCol(value: String): XGBoostRegressionModel.this.type
  163. def setMissing(value: Float): XGBoostRegressionModel.this.type
  164. def setParent(parent: Estimator[XGBoostRegressionModel]): XGBoostRegressionModel
    Definition Classes
    Model
  165. def setPredictionCol(value: String): XGBoostRegressionModel
    Definition Classes
    PredictionModel
  166. def setTreeLimit(value: Int): XGBoostRegressionModel.this.type
  167. final val silent: IntParam

    Deprecated.

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

    Definition Classes
    GeneralParams
  168. 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
  169. 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
  170. 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
  171. 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
  172. 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.

  173. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  174. 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
  175. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  176. 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
  177. final val trainTestRatio: DoubleParam

    Fraction of training points to use for testing.

    Fraction of training points to use for testing.

    Definition Classes
    LearningTaskParams
  178. def transform(dataset: Dataset[_]): DataFrame
    Definition Classes
    XGBoostRegressionModel → PredictionModel → Transformer
  179. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  180. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  181. def transformImpl(dataset: Dataset[_]): DataFrame
    Attributes
    protected
    Definition Classes
    PredictionModel
  182. def transformSchema(schema: StructType): StructType
    Definition Classes
    PredictionModel → PipelineStage
  183. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  184. final val treeLimit: IntParam
    Definition Classes
    BoosterParams
  185. 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
  186. val uid: String
    Definition Classes
    XGBoostRegressionModel → Identifiable
  187. final val useExternalMemory: BooleanParam

    whether to use external memory as cache.

    whether to use external memory as cache. default: false

    Definition Classes
    GeneralParams
  188. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
    Attributes
    protected
    Definition Classes
    PredictorParams
  189. 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
  190. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  191. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  192. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  193. final val weightCol: Param[String]
    Definition Classes
    HasWeightCol
  194. def write: MLWriter
    Definition Classes
    XGBoostRegressionModel → MLWritable

Inherited from MLWritable

Inherited from InferenceParams

Inherited from XGBoostRegressorParams

Inherited from NonParamVariables

Inherited from HasContribPredictionCol

Inherited from HasLeafPredictionCol

Inherited from ParamMapFuncs

Inherited from HasGroupCol

Inherited from HasWeightCol

Inherited from HasBaseMarginCol

Inherited from LearningTaskParams

Inherited from BoosterParams

Inherited from GeneralParams

Inherited from PredictionModel[Vector, XGBoostRegressionModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Model[XGBoostRegressionModel]

Inherited from Transformer

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

getParam

param

Ungrouped