Packages

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

Linear Supertypes
DefaultParamsWritable, MLWritable, XGBoostRegressorParams, HasGroupCol, XGBoostEstimatorCommon, HasHandleInvalid, HasFeaturesCols, HasContribPredictionCol, HasLeafPredictionCol, HasBaseMarginCol, HasWeightCol, NonParamVariables, ParamMapFuncs, RabitParams, BoosterParams, LearningTaskParams, 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. HasGroupCol
  6. XGBoostEstimatorCommon
  7. HasHandleInvalid
  8. HasFeaturesCols
  9. HasContribPredictionCol
  10. HasLeafPredictionCol
  11. HasBaseMarginCol
  12. HasWeightCol
  13. NonParamVariables
  14. ParamMapFuncs
  15. RabitParams
  16. BoosterParams
  17. LearningTaskParams
  18. GeneralParams
  19. Predictor
  20. PredictorParams
  21. HasPredictionCol
  22. HasFeaturesCol
  23. HasLabelCol
  24. Estimator
  25. PipelineStage
  26. Logging
  27. Params
  28. Serializable
  29. Serializable
  30. Identifiable
  31. AnyRef
  32. 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 XGBoost2MLlibParams(xgboostParams: Map[String, Any]): Unit
    Definition Classes
    ParamMapFuncs
  7. final val allowNonZeroForMissing: BooleanParam

    Allows for having a non-zero value for missing when training on prediction on a Sparse or Empty vector.

    Allows for having a non-zero value for missing when training on prediction on a Sparse or Empty vector.

    Definition Classes
    GeneralParams
  8. 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
  9. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  10. 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
  11. 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
  12. final val cacheTrainingSet: BooleanParam

    whether caching training data

    whether caching training data

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

    Param for contribution prediction column name.

    Param for contribution prediction column name.

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

    customized evaluation function provided by user.

    customized evaluation function provided by user. default: null

    Definition Classes
    GeneralParams
  23. final val customObj: CustomObjParam

    customized objective function provided by user.

    customized objective function provided by user. default: null

    Definition Classes
    GeneralParams
  24. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  25. final val device: Param[String]

    The device for running XGBoost algorithms, options: cpu, cuda

    The device for running XGBoost algorithms, options: cpu, cuda

    Definition Classes
    BoosterParams
  26. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  27. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  28. 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
  29. 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, mape, logloss, error, merror, mlogloss, auc, aucpr, ndcg, map, gamma-deviance

    Definition Classes
    LearningTaskParams
  30. val evalSetsMap: Map[String, DataFrame]
    Attributes
    protected
    Definition Classes
    NonParamVariables
  31. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  32. def explainParams(): String
    Definition Classes
    Params
  33. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  34. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  35. final val featureNames: StringArrayParam

    Feature's name, it will be set to DMatrix and Booster, and in the final native json model.

    Feature's name, it will be set to DMatrix and Booster, and in the final native json model. In native code, the parameter name is feature_name.

    Definition Classes
    GeneralParams
  36. final val featureTypes: StringArrayParam

    Feature types, q is numeric and c is categorical.

    Feature types, q is numeric and c is categorical. In native code, the parameter name is feature_type

    Definition Classes
    GeneralParams
  37. final val featuresCol: Param[String]
    Definition Classes
    HasFeaturesCol
  38. final val featuresCols: StringArrayParam

    Param for the names of feature columns.

    Param for the names of feature columns.

    Definition Classes
    HasFeaturesCols
  39. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  40. def fit(dataset: Dataset[_]): XGBoostRegressionModel
    Definition Classes
    Predictor → Estimator
  41. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[XGBoostRegressionModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  42. def fit(dataset: Dataset[_], paramMap: ParamMap): XGBoostRegressionModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  43. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): XGBoostRegressionModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  44. 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
  45. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  46. final def getAllowNonZeroForMissingValue: Boolean
    Definition Classes
    GeneralParams
  47. final def getAlpha: Double
    Definition Classes
    BoosterParams
  48. final def getBaseMarginCol: String

    Definition Classes
    HasBaseMarginCol
  49. final def getBaseScore: Double
    Definition Classes
    LearningTaskParams
  50. final def getCheckpointInterval: Int
    Definition Classes
    GeneralParams
  51. final def getCheckpointPath: String
    Definition Classes
    GeneralParams
  52. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  53. final def getColsampleBylevel: Double
    Definition Classes
    BoosterParams
  54. final def getColsampleBytree: Double
    Definition Classes
    BoosterParams
  55. final def getContribPredictionCol: String

    Definition Classes
    HasContribPredictionCol
  56. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  57. final def getDevice: String
    Definition Classes
    BoosterParams
  58. final def getEta: Double
    Definition Classes
    BoosterParams
  59. final def getEvalMetric: String
    Definition Classes
    LearningTaskParams
  60. def getEvalSets(params: Map[String, Any]): Map[String, DataFrame]
    Definition Classes
    NonParamVariables
  61. final def getFeatureNames: Array[String]
    Definition Classes
    GeneralParams
  62. final def getFeatureTypes: Array[String]
    Definition Classes
    GeneralParams
  63. final def getFeaturesCol: String
    Definition Classes
    HasFeaturesCol
  64. final def getFeaturesCols: Array[String]

    Definition Classes
    HasFeaturesCols
  65. final def getGamma: Double
    Definition Classes
    BoosterParams
  66. final def getGroupCol: String

    Definition Classes
    HasGroupCol
  67. final def getGrowPolicy: String
    Definition Classes
    BoosterParams
  68. final def getHandleInvalid: String
    Definition Classes
    HasHandleInvalid
  69. final def getInteractionConstraints: String
    Definition Classes
    BoosterParams
  70. final def getLabelCol: String
    Definition Classes
    HasLabelCol
  71. final def getLambda: Double
    Definition Classes
    BoosterParams
  72. final def getLambdaBias: Double
    Definition Classes
    BoosterParams
  73. final def getLeafPredictionCol: String

    Definition Classes
    HasLeafPredictionCol
  74. final def getMaxBins: Int
    Definition Classes
    BoosterParams
  75. final def getMaxDeltaStep: Double
    Definition Classes
    BoosterParams
  76. final def getMaxDepth: Int
    Definition Classes
    BoosterParams
  77. final def getMaxLeaves: Int
    Definition Classes
    BoosterParams
  78. final def getMaximizeEvaluationMetrics: Boolean
    Definition Classes
    LearningTaskParams
  79. final def getMinChildWeight: Double
    Definition Classes
    BoosterParams
  80. final def getMissing: Float
    Definition Classes
    GeneralParams
  81. final def getMonotoneConstraints: String
    Definition Classes
    BoosterParams
  82. final def getNormalizeType: String
    Definition Classes
    BoosterParams
  83. final def getNthread: Int
    Definition Classes
    GeneralParams
  84. final def getNumEarlyStoppingRounds: Int
    Definition Classes
    LearningTaskParams
  85. final def getNumRound: Int
    Definition Classes
    GeneralParams
  86. final def getNumWorkers: Int
    Definition Classes
    GeneralParams
  87. final def getObjective: String
    Definition Classes
    LearningTaskParams
  88. final def getObjectiveType: String
    Definition Classes
    LearningTaskParams
  89. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  90. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  91. final def getPredictionCol: String
    Definition Classes
    HasPredictionCol
  92. final def getRateDrop: Double
    Definition Classes
    BoosterParams
  93. final def getSampleType: String
    Definition Classes
    BoosterParams
  94. final def getScalePosWeight: Double
    Definition Classes
    BoosterParams
  95. final def getSeed: Long
    Definition Classes
    GeneralParams
  96. final def getSilent: Int
    Definition Classes
    GeneralParams
  97. final def getSinglePrecisionHistogram: Boolean
    Definition Classes
    BoosterParams
  98. final def getSkipDrop: Double
    Definition Classes
    BoosterParams
  99. final def getSubsample: Double
    Definition Classes
    BoosterParams
  100. final def getTrainTestRatio: Double
    Definition Classes
    LearningTaskParams
    Annotations
    @Deprecated
  101. final def getTreeLimit: Int
    Definition Classes
    BoosterParams
  102. final def getTreeMethod: String
    Definition Classes
    BoosterParams
  103. final def getUseExternalMemory: Boolean
    Definition Classes
    GeneralParams
  104. final def getVerbosity: Int
    Definition Classes
    GeneralParams
  105. final def getWeightCol: String
    Definition Classes
    HasWeightCol
  106. final val groupCol: Param[String]

    Param for group column name.

    Param for group column name.

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

    growth policy for fast histogram algorithm

    growth policy for fast histogram algorithm

    Definition Classes
    BoosterParams
  108. val handleInvalid: Param[String]

    Param for how to handle invalid data (NULL values).

    Param for how to handle invalid data (NULL values). Options are 'skip' (filter out rows with invalid data), 'error' (throw an error), or 'keep' (return relevant number of NaN in the output). Column lengths are taken from the size of ML Attribute Group, which can be set using VectorSizeHint in a pipeline before VectorAssembler. Column lengths can also be inferred from first rows of the data since it is safe to do so but only in case of 'error' or 'skip'. Default: "error"

    Definition Classes
    XGBoostEstimatorCommon → HasHandleInvalid
  109. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  110. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  111. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  112. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  113. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  114. final val interactionConstraints: Param[String]
    Definition Classes
    BoosterParams
  115. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  116. def isFeaturesColSet(schema: StructType): Boolean

    Check if schema has a field named with the value of "featuresCol" param and it's data type must be VectorUDT

    Check if schema has a field named with the value of "featuresCol" param and it's data type must be VectorUDT

    Definition Classes
    XGBoostEstimatorCommon
  117. def isFeaturesColsValid: Boolean

    Check if featuresCols is valid

    Check if featuresCols is valid

    Definition Classes
    HasFeaturesCols
  118. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  119. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  120. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  121. final val labelCol: Param[String]
    Definition Classes
    HasLabelCol
  122. 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
  123. 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
  124. final val leafPredictionCol: Param[String]

    Param for leaf prediction column name.

    Param for leaf prediction column name.

    Definition Classes
    HasLeafPredictionCol
  125. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  126. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  127. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  128. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  129. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  130. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  131. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  132. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  133. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  134. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  135. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  136. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  137. final val maxBins: IntParam

    maximum number of bins in histogram

    maximum number of bins in histogram

    Definition Classes
    BoosterParams
  138. 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
  139. 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
  140. 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
  141. final val maximizeEvaluationMetrics: BooleanParam
    Definition Classes
    LearningTaskParams
  142. 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
  143. final val missing: FloatParam

    the value treated as missing.

    the value treated as missing. default: Float.NaN

    Definition Classes
    GeneralParams
  144. final val monotoneConstraints: Param[String]
    Definition Classes
    BoosterParams
  145. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  146. def needDeterministicRepartitioning: Boolean
    Definition Classes
    XGBoostEstimatorCommon
  147. 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
  148. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  149. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  150. final val nthread: IntParam

    number of threads used by per worker.

    number of threads used by per worker. default 1

    Definition Classes
    GeneralParams
  151. 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
  152. final val numRound: IntParam

    The number of rounds for boosting

    The number of rounds for boosting

    Definition Classes
    GeneralParams
  153. 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
  154. 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:ndcg, reg:gamma. default: reg:squarederror

    Definition Classes
    LearningTaskParams
  155. 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
  156. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  157. final val predictionCol: Param[String]
    Definition Classes
    HasPredictionCol
  158. final def rabitConnectRetry: IntParam
    Definition Classes
    RabitParams
  159. final val rabitRingReduceThreshold: IntParam

    Rabit parameters passed through Rabit.Init into native layer rabit_ring_reduce_threshold - minimal threshold to enable ring based allreduce operation rabit_timeout - wait interval before exit after rabit observed failures set -1 to disable dmlc_worker_connect_retry - number of retrys to tracker dmlc_worker_stop_process_on_error - exit process when rabit see assert/error

    Rabit parameters passed through Rabit.Init into native layer rabit_ring_reduce_threshold - minimal threshold to enable ring based allreduce operation rabit_timeout - wait interval before exit after rabit observed failures set -1 to disable dmlc_worker_connect_retry - number of retrys to tracker dmlc_worker_stop_process_on_error - exit process when rabit see assert/error

    Definition Classes
    RabitParams
  160. final def rabitTimeout: IntParam
    Definition Classes
    RabitParams
  161. 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
  162. 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
  163. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  164. 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
  165. 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
  166. final def set(paramPair: ParamPair[_]): XGBoostRegressor.this.type
    Attributes
    protected
    Definition Classes
    Params
  167. final def set(param: String, value: Any): XGBoostRegressor.this.type
    Attributes
    protected
    Definition Classes
    Params
  168. final def set[T](param: Param[T], value: T): XGBoostRegressor.this.type
    Definition Classes
    Params
  169. def setAllowNonZeroForMissing(value: Boolean): XGBoostRegressor.this.type
  170. def setAlpha(value: Double): XGBoostRegressor.this.type
  171. def setBaseMarginCol(value: String): XGBoostRegressor.this.type
  172. def setBaseScore(value: Double): XGBoostRegressor.this.type
  173. def setCheckpointInterval(value: Int): XGBoostRegressor.this.type
  174. def setCheckpointPath(value: String): XGBoostRegressor.this.type
  175. def setColsampleBylevel(value: Double): XGBoostRegressor.this.type
  176. def setColsampleBytree(value: Double): XGBoostRegressor.this.type
  177. def setCustomEval(value: EvalTrait): XGBoostRegressor.this.type
  178. def setCustomObj(value: ObjectiveTrait): XGBoostRegressor.this.type
  179. final def setDefault(paramPairs: ParamPair[_]*): XGBoostRegressor.this.type
    Attributes
    protected
    Definition Classes
    Params
  180. final def setDefault[T](param: Param[T], value: T): XGBoostRegressor.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  181. def setDevice(value: String): XGBoostRegressor.this.type
  182. def setEta(value: Double): XGBoostRegressor.this.type
  183. def setEvalMetric(value: String): XGBoostRegressor.this.type
  184. def setEvalSets(evalSets: Map[String, DataFrame]): XGBoostRegressor.this.type
    Definition Classes
    NonParamVariables
  185. def setFeatureNames(value: Array[String]): XGBoostRegressor.this.type
  186. def setFeatureTypes(value: Array[String]): XGBoostRegressor.this.type
  187. def setFeaturesCol(value: Array[String]): XGBoostRegressor.this.type

    Specify an array of feature column names which must be numeric types.

    Specify an array of feature column names which must be numeric types.

    Definition Classes
    XGBoostEstimatorCommon
  188. def setFeaturesCol(value: String): XGBoostRegressor
    Definition Classes
    Predictor
  189. def setGamma(value: Double): XGBoostRegressor.this.type
  190. def setGroupCol(value: String): XGBoostRegressor.this.type
  191. def setGrowPolicy(value: String): XGBoostRegressor.this.type
  192. def setHandleInvalid(value: String): XGBoostRegressor.this.type

    Set the handleInvalid for VectorAssembler

    Set the handleInvalid for VectorAssembler

    Definition Classes
    XGBoostEstimatorCommon
  193. def setLabelCol(value: String): XGBoostRegressor
    Definition Classes
    Predictor
  194. def setLambda(value: Double): XGBoostRegressor.this.type
  195. def setLambdaBias(value: Double): XGBoostRegressor.this.type
  196. def setMaxBins(value: Int): XGBoostRegressor.this.type
  197. def setMaxDeltaStep(value: Double): XGBoostRegressor.this.type
  198. def setMaxDepth(value: Int): XGBoostRegressor.this.type
  199. def setMaxLeaves(value: Int): XGBoostRegressor.this.type
  200. def setMaximizeEvaluationMetrics(value: Boolean): XGBoostRegressor.this.type
  201. def setMinChildWeight(value: Double): XGBoostRegressor.this.type
  202. def setMissing(value: Float): XGBoostRegressor.this.type
  203. def setNormalizeType(value: String): XGBoostRegressor.this.type
  204. def setNthread(value: Int): XGBoostRegressor.this.type
  205. def setNumEarlyStoppingRounds(value: Int): XGBoostRegressor.this.type
  206. def setNumRound(value: Int): XGBoostRegressor.this.type
  207. def setNumWorkers(value: Int): XGBoostRegressor.this.type
  208. def setObjective(value: String): XGBoostRegressor.this.type
  209. def setObjectiveType(value: String): XGBoostRegressor.this.type
  210. def setPredictionCol(value: String): XGBoostRegressor
    Definition Classes
    Predictor
  211. def setRateDrop(value: Double): XGBoostRegressor.this.type
  212. def setSampleType(value: String): XGBoostRegressor.this.type
  213. def setScalePosWeight(value: Double): XGBoostRegressor.this.type
  214. def setSeed(value: Long): XGBoostRegressor.this.type
  215. def setSilent(value: Int): XGBoostRegressor.this.type
  216. def setSinglePrecisionHistogram(value: Boolean): XGBoostRegressor.this.type
  217. def setSkipDrop(value: Double): XGBoostRegressor.this.type
  218. def setSubsample(value: Double): XGBoostRegressor.this.type
  219. def setTrainTestRatio(value: Double): XGBoostRegressor.this.type
  220. def setTreeMethod(value: String): XGBoostRegressor.this.type
  221. def setUseExternalMemory(value: Boolean): XGBoostRegressor.this.type
  222. def setWeightCol(value: String): XGBoostRegressor.this.type
  223. final val silent: IntParam

    Deprecated.

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

    Definition Classes
    GeneralParams
  224. final val singlePrecisionHistogram: BooleanParam

    whether to build histograms using single precision floating point values

    whether to build histograms using single precision floating point values

    Definition Classes
    BoosterParams
  225. 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
  226. 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
  227. 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
  228. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  229. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  230. 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
  231. def train(dataset: Dataset[_]): XGBoostRegressionModel
    Attributes
    protected
    Definition Classes
    XGBoostRegressor → Predictor
  232. final val trainTestRatio: DoubleParam

    Fraction of training points to use for testing.

    Fraction of training points to use for testing.

    Definition Classes
    LearningTaskParams
    Annotations
    @Deprecated
  233. def transformSchema(schema: StructType): StructType
    Definition Classes
    XGBoostRegressor → Predictor → PipelineStage
  234. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  235. def transformSchemaWithFeaturesCols(fit: Boolean, schema: StructType): StructType

    check the features columns type

    check the features columns type

    Definition Classes
    XGBoostEstimatorCommon
  236. final val treeLimit: IntParam
    Definition Classes
    BoosterParams
  237. final val treeMethod: Param[String]

    The tree construction algorithm used in XGBoost.

    The tree construction algorithm used in XGBoost. options: {'auto', 'exact', 'approx','gpu_hist'} [default='auto']

    Definition Classes
    BoosterParams
  238. val uid: String
    Definition Classes
    XGBoostRegressor → Identifiable
  239. final val useExternalMemory: BooleanParam

    whether to use external memory as cache.

    whether to use external memory as cache. default: false

    Definition Classes
    GeneralParams
  240. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
    Attributes
    protected
    Definition Classes
    PredictorParams
  241. def vectorize(input: Dataset[_]): (Dataset[_], String)

    Vectorize the features columns if necessary.

    Vectorize the features columns if necessary.

    input

    the input dataset

    returns

    (output dataset and the feature column name)

    Definition Classes
    XGBoostEstimatorCommon
  242. 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
  243. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  244. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  245. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  246. final val weightCol: Param[String]
    Definition Classes
    HasWeightCol
  247. def write: MLWriter
    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from XGBoostRegressorParams

Inherited from HasGroupCol

Inherited from XGBoostEstimatorCommon

Inherited from HasHandleInvalid

Inherited from HasFeaturesCols

Inherited from HasContribPredictionCol

Inherited from HasLeafPredictionCol

Inherited from HasBaseMarginCol

Inherited from HasWeightCol

Inherited from NonParamVariables

Inherited from ParamMapFuncs

Inherited from RabitParams

Inherited from BoosterParams

Inherited from LearningTaskParams

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