xgboost4j-spark_2.12 1.2.0 API < Back
 

# Packages

-  package root
  Definition Classes
    root
-  package ml
  Definition Classes
    root
-  package dmlc
  Definition Classes
    ml
-  package xgboost4j
  Definition Classes
    dmlc
-  package scala
  Definition Classes
    xgboost4j
-  package spark
  Definition Classes
    scala
-  package params
  Definition Classes
    spark
- DataUtils
- TrackerConf
- XGBoost
- XGBoostClassificationModel
- XGBoostClassifier
- XGBoostRegressionModel
- XGBoostRegressor
- XGBoostTrainingSummary

c

ml.dmlc.xgboost4j.scala.spark

# XGBoostRegressor 

###
Companion object XGBoostRegressor

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

Linear Supertypes
DefaultParamsWritable, MLWritable, XGBoostRegressorParams, XGBoostEstimatorCommon, NonParamVariables, ParamMapFuncs, RabitParams, BoosterParams, LearningTaskParams, GeneralParams, HasContribPredictionCol, HasLeafPredictionCol, HasGroupCol, HasWeightCol, HasBaseMarginCol, 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. XGBoostEstimatorCommon
6. NonParamVariables
7. ParamMapFuncs
8. RabitParams
9. BoosterParams
10. LearningTaskParams
11. GeneralParams
12. HasContribPredictionCol
13. HasLeafPredictionCol
14. HasGroupCol
15. HasWeightCol
16. HasBaseMarginCol
17. Predictor
18. PredictorParams
19. HasPredictionCol
20. HasFeaturesCol
21. HasLabelCol
22. Estimator
23. PipelineStage
24. Logging
25. Params
26. Serializable
27. Serializable
28. Identifiable
29. AnyRef
30. 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[java.lang]
    Definition Classes
      AnyRef
    Annotations
      @native() @throws( ... )
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 def eq(arg0: AnyRef): Boolean
    Definition Classes
      AnyRef
26.  def equals(arg0: Any): Boolean
    Definition Classes
      AnyRef → Any
27.  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
28.  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
29.  val evalSetsMap: Map[String, DataFrame]
    Attributes
      protected
    Definition Classes
      NonParamVariables
30.  def explainParam(param: Param[_]): String
    Definition Classes
      Params
31.  def explainParams(): String
    Definition Classes
      Params
32.  def extractInstances(dataset: Dataset[_], validateInstance: (Instance) ⇒ Unit): RDD[Instance]
    Attributes
      protected
    Definition Classes
      PredictorParams
33.  def extractInstances(dataset: Dataset[_]): RDD[Instance]
    Attributes
      protected
    Definition Classes
      PredictorParams
34.  def extractLabeledPoints(dataset: Dataset[_]): RDD[org.apache.spark.ml.feature.LabeledPoint]
    Attributes
      protected
    Definition Classes
      Predictor
35.  final def extractParamMap(): ParamMap
    Definition Classes
      Params
36.  final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
      Params
37.  final val featuresCol: Param[String]
    Definition Classes
      HasFeaturesCol
38.  def finalize(): Unit
    Attributes
      protected[java.lang]
    Definition Classes
      AnyRef
    Annotations
      @throws( classOf[java.lang.Throwable] )
39.  def fit(dataset: Dataset[_]): XGBoostRegressionModel
    Definition Classes
      Predictor → Estimator
40.  def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[XGBoostRegressionModel]
    Definition Classes
      Estimator
    Annotations
      @Since( "2.0.0" )
41.  def fit(dataset: Dataset[_], paramMap: ParamMap): XGBoostRegressionModel
    Definition Classes
      Estimator
    Annotations
      @Since( "2.0.0" )
42.  def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): XGBoostRegressionModel
    Definition Classes
      Estimator
    Annotations
      @Since( "2.0.0" ) @varargs()
43.  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
44.  final def get[T](param: Param[T]): Option[T]
    Definition Classes
      Params
45.  final def getAllowNonZeroForMissingValue: Boolean
    Definition Classes
      GeneralParams
46.  final def getAlpha: Double
    Definition Classes
      BoosterParams
47.  final def getBaseMarginCol: String

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

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

    Definition Classes
      HasGroupCol
62.  final def getGrowPolicy: String
    Definition Classes
      BoosterParams
63.  final def getInteractionConstraints: String
    Definition Classes
      BoosterParams
64.  final def getLabelCol: String
    Definition Classes
      HasLabelCol
65.  final def getLambda: Double
    Definition Classes
      BoosterParams
66.  final def getLambdaBias: Double
    Definition Classes
      BoosterParams
67.  final def getLeafPredictionCol: String

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

     Param for group column name.

     Param for group column name.

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

     growth policy for fast histogram algorithm

     growth policy for fast histogram algorithm

     Definition Classes
       BoosterParams
104.  final def hasDefault[T](param: Param[T]): Boolean
     Definition Classes
       Params
105.  def hasParam(paramName: String): Boolean
     Definition Classes
       Params
106.  def hashCode(): Int
     Definition Classes
       AnyRef → Any
     Annotations
       @native()
107.  def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
     Attributes
       protected
     Definition Classes
       Logging
108.  def initializeLogIfNecessary(isInterpreter: Boolean): Unit
     Attributes
       protected
     Definition Classes
       Logging
109.  final val interactionConstraints: Param[String]
     Definition Classes
       BoosterParams
110.  final def isDefined(param: Param[_]): Boolean
     Definition Classes
       Params
111.  final def isInstanceOf[T0]: Boolean
     Definition Classes
       Any
112.  final def isSet(param: Param[_]): Boolean
     Definition Classes
       Params
113.  def isTraceEnabled(): Boolean
     Attributes
       protected
     Definition Classes
       Logging
114.  final val labelCol: Param[String]
     Definition Classes
       HasLabelCol
115.  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
116.  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
117.  final val leafPredictionCol: Param[String]

     Param for leaf prediction column name.

     Param for leaf prediction column name.

     Definition Classes
       HasLeafPredictionCol
118.  def log: Logger
     Attributes
       protected
     Definition Classes
       Logging
119.  def logDebug(msg: ⇒ String, throwable: Throwable): Unit
     Attributes
       protected
     Definition Classes
       Logging
120.  def logDebug(msg: ⇒ String): Unit
     Attributes
       protected
     Definition Classes
       Logging
121.  def logError(msg: ⇒ String, throwable: Throwable): Unit
     Attributes
       protected
     Definition Classes
       Logging
122.  def logError(msg: ⇒ String): Unit
     Attributes
       protected
     Definition Classes
       Logging
123.  def logInfo(msg: ⇒ String, throwable: Throwable): Unit
     Attributes
       protected
     Definition Classes
       Logging
124.  def logInfo(msg: ⇒ String): Unit
     Attributes
       protected
     Definition Classes
       Logging
125.  def logName: String
     Attributes
       protected
     Definition Classes
       Logging
126.  def logTrace(msg: ⇒ String, throwable: Throwable): Unit
     Attributes
       protected
     Definition Classes
       Logging
127.  def logTrace(msg: ⇒ String): Unit
     Attributes
       protected
     Definition Classes
       Logging
128.  def logWarning(msg: ⇒ String, throwable: Throwable): Unit
     Attributes
       protected
     Definition Classes
       Logging
129.  def logWarning(msg: ⇒ String): Unit
     Attributes
       protected
     Definition Classes
       Logging
130.  final val maxBins: IntParam

     maximum number of bins in histogram

     maximum number of bins in histogram

     Definition Classes
       BoosterParams
131.  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
132.  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
133.  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
134.  final val maximizeEvaluationMetrics: BooleanParam
     Definition Classes
       LearningTaskParams
135.  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
136.  final val missing: FloatParam

     the value treated as missing.

     the value treated as missing. default: Float.NaN

     Definition Classes
       GeneralParams
137.  final val monotoneConstraints: Param[String]
     Definition Classes
       BoosterParams
138.  final def ne(arg0: AnyRef): Boolean
     Definition Classes
       AnyRef
139.  def needDeterministicRepartitioning: Boolean
     Definition Classes
       XGBoostEstimatorCommon
140.  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
141.  final def notify(): Unit
     Definition Classes
       AnyRef
     Annotations
       @native()
142.  final def notifyAll(): Unit
     Definition Classes
       AnyRef
     Annotations
       @native()
143.  final val nthread: IntParam

     number of threads used by per worker.

     number of threads used by per worker. default 1

     Definition Classes
       GeneralParams
144.  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
145.  final val numRound: IntParam

     The number of rounds for boosting

     The number of rounds for boosting

     Definition Classes
       GeneralParams
146.  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
147.  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
148.  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
149.  lazy val params: Array[Param[_]]
     Definition Classes
       Params
150.  final val predictionCol: Param[String]
     Definition Classes
       HasPredictionCol
151.  final def rabitConnectRetry: IntParam
     Definition Classes
       RabitParams
152.  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
153.  final def rabitTimeout: IntParam
     Definition Classes
       RabitParams
154.  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
155.  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
156.  def save(path: String): Unit
     Definition Classes
       MLWritable
     Annotations
       @Since( "1.6.0" ) @throws( ... )
157.  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
158.  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
159.  final def set(paramPair: ParamPair[_]): XGBoostRegressor.this.type
     Attributes
       protected
     Definition Classes
       Params
160.  final def set(param: String, value: Any): XGBoostRegressor.this.type
     Attributes
       protected
     Definition Classes
       Params
161.  final def set[T](param: Param[T], value: T): XGBoostRegressor.this.type
     Definition Classes
       Params
162.  def setAllowNonZeroForMissing(value: Boolean): XGBoostRegressor.this.type
163.  def setAlpha(value: Double): XGBoostRegressor.this.type
164.  def setBaseMarginCol(value: String): XGBoostRegressor.this.type
165.  def setBaseScore(value: Double): XGBoostRegressor.this.type
166.  def setCheckpointInterval(value: Int): XGBoostRegressor.this.type
167.  def setCheckpointPath(value: String): XGBoostRegressor.this.type
168.  def setColsampleBylevel(value: Double): XGBoostRegressor.this.type
169.  def setColsampleBytree(value: Double): XGBoostRegressor.this.type
170.  def setCustomEval(value: EvalTrait): XGBoostRegressor.this.type
171.  def setCustomObj(value: ObjectiveTrait): XGBoostRegressor.this.type
172.  final def setDefault(paramPairs: ParamPair[_]*): XGBoostRegressor.this.type
     Attributes
       protected
     Definition Classes
       Params
173.  final def setDefault[T](param: Param[T], value: T): XGBoostRegressor.this.type
     Attributes
       protected
     Definition Classes
       Params
174.  def setEta(value: Double): XGBoostRegressor.this.type
175.  def setEvalMetric(value: String): XGBoostRegressor.this.type
176.  def setEvalSets(evalSets: Map[String, DataFrame]): XGBoostRegressor.this.type
     Definition Classes
       NonParamVariables
177.  def setFeaturesCol(value: String): XGBoostRegressor
     Definition Classes
       Predictor
178.  def setGamma(value: Double): XGBoostRegressor.this.type
179.  def setGroupCol(value: String): XGBoostRegressor.this.type
180.  def setGrowPolicy(value: String): XGBoostRegressor.this.type
181.  def setLabelCol(value: String): XGBoostRegressor
     Definition Classes
       Predictor
182.  def setLambda(value: Double): XGBoostRegressor.this.type
183.  def setLambdaBias(value: Double): XGBoostRegressor.this.type
184.  def setMaxBins(value: Int): XGBoostRegressor.this.type
185.  def setMaxDeltaStep(value: Double): XGBoostRegressor.this.type
186.  def setMaxDepth(value: Int): XGBoostRegressor.this.type
187.  def setMaxLeaves(value: Int): XGBoostRegressor.this.type
188.  def setMaximizeEvaluationMetrics(value: Boolean): XGBoostRegressor.this.type
189.  def setMinChildWeight(value: Double): XGBoostRegressor.this.type
190.  def setMissing(value: Float): XGBoostRegressor.this.type
191.  def setNormalizeType(value: String): XGBoostRegressor.this.type
192.  def setNthread(value: Int): XGBoostRegressor.this.type
193.  def setNumEarlyStoppingRounds(value: Int): XGBoostRegressor.this.type
194.  def setNumRound(value: Int): XGBoostRegressor.this.type
195.  def setNumWorkers(value: Int): XGBoostRegressor.this.type
196.  def setObjective(value: String): XGBoostRegressor.this.type
197.  def setObjectiveType(value: String): XGBoostRegressor.this.type
198.  def setPredictionCol(value: String): XGBoostRegressor
     Definition Classes
       Predictor
199.  def setRateDrop(value: Double): XGBoostRegressor.this.type
200.  def setSampleType(value: String): XGBoostRegressor.this.type
201.  def setScalePosWeight(value: Double): XGBoostRegressor.this.type
202.  def setSeed(value: Long): XGBoostRegressor.this.type
203.  def setSilent(value: Int): XGBoostRegressor.this.type
204.  def setSinglePrecisionHistogram(value: Boolean): XGBoostRegressor.this.type
205.  def setSketchEps(value: Double): XGBoostRegressor.this.type
206.  def setSkipDrop(value: Double): XGBoostRegressor.this.type
207.  def setSubsample(value: Double): XGBoostRegressor.this.type
208.  def setTimeoutRequestWorkers(value: Long): XGBoostRegressor.this.type
209.  def setTrainTestRatio(value: Double): XGBoostRegressor.this.type
210.  def setTreeMethod(value: String): XGBoostRegressor.this.type
211.  def setUseExternalMemory(value: Boolean): XGBoostRegressor.this.type
212.  def setWeightCol(value: String): XGBoostRegressor.this.type
213.  final val silent: IntParam

     Deprecated.

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

     Definition Classes
       GeneralParams
214.  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
215.  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
216.  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
217.  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
218.  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
219.  final def synchronized[T0](arg0: ⇒ T0): T0
     Definition Classes
       AnyRef
220.  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
221.  def toString(): String
     Definition Classes
       Identifiable → AnyRef → Any
222.  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
223.  def train(dataset: Dataset[_]): XGBoostRegressionModel
     Attributes
       protected
     Definition Classes
       XGBoostRegressor → Predictor
224.  final val trainTestRatio: DoubleParam

     Fraction of training points to use for testing.

     Fraction of training points to use for testing.

     Definition Classes
       LearningTaskParams
225.  def transformSchema(schema: StructType): StructType
     Definition Classes
       Predictor → PipelineStage
226.  def transformSchema(schema: StructType, logging: Boolean): StructType
     Attributes
       protected
     Definition Classes
       PipelineStage
     Annotations
       @DeveloperApi()
227.  final val treeLimit: IntParam
     Definition Classes
       BoosterParams
228.  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
229.  val uid: String
     Definition Classes
       XGBoostRegressor → Identifiable
230.  final val useExternalMemory: BooleanParam

     whether to use external memory as cache.

     whether to use external memory as cache. default: false

     Definition Classes
       GeneralParams
231.  def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
     Attributes
       protected
     Definition Classes
       PredictorParams
232.  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
233.  final def wait(): Unit
     Definition Classes
       AnyRef
     Annotations
       @throws( ... )
234.  final def wait(arg0: Long, arg1: Int): Unit
     Definition Classes
       AnyRef
     Annotations
       @throws( ... )
235.  final def wait(arg0: Long): Unit
     Definition Classes
       AnyRef
     Annotations
       @native() @throws( ... )
236.  final val weightCol: Param[String]
     Definition Classes
       HasWeightCol
237.  def write: MLWriter
     Definition Classes
       DefaultParamsWritable → MLWritable

### Inherited from DefaultParamsWritable

### Inherited from MLWritable

### Inherited from XGBoostRegressorParams

### Inherited from XGBoostEstimatorCommon

### Inherited from NonParamVariables

### Inherited from ParamMapFuncs

### Inherited from RabitParams

### Inherited from BoosterParams

### Inherited from LearningTaskParams

### Inherited from GeneralParams

### Inherited from HasContribPredictionCol

### Inherited from HasLeafPredictionCol

### Inherited from HasGroupCol

### Inherited from HasWeightCol

### Inherited from HasBaseMarginCol

### 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 Identifiable

### Inherited from AnyRef

### Inherited from Any

### getParam

### param

### Ungrouped