xgboost4j-spark_2.12 1.3.3 API < Back
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# 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

# XGBoostClassifier 

###
Companion object XGBoostClassifier

#### class XGBoostClassifier extends ProbabilisticClassifier[Vector, XGBoostClassifier, XGBoostClassificationModel] with XGBoostClassifierParams with DefaultParamsWritable

Linear Supertypes
DefaultParamsWritable, MLWritable, XGBoostClassifierParams, XGBoostEstimatorCommon, NonParamVariables, ParamMapFuncs, RabitParams, BoosterParams, LearningTaskParams, GeneralParams, HasContribPredictionCol, HasLeafPredictionCol, HasNumClass, HasBaseMarginCol, HasWeightCol, ProbabilisticClassifier[Vector, XGBoostClassifier, XGBoostClassificationModel], ProbabilisticClassifierParams, HasThresholds, HasProbabilityCol, Classifier[Vector, XGBoostClassifier, XGBoostClassificationModel], ClassifierParams, HasRawPredictionCol, Predictor[Vector, XGBoostClassifier, XGBoostClassificationModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Estimator[XGBoostClassificationModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited

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

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Visibility

1. Public
2. All

### Instance Constructors

1.  new XGBoostClassifier(xgboostParams: Map[String, Any])
2.  new XGBoostClassifier(uid: String)
3.  new XGBoostClassifier()
4.  new XGBoostClassifier(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[_]): XGBoostClassifier.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): XGBoostClassifier
    Definition Classes
      XGBoostClassifier → 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, mape, 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[_], numClasses: Int): RDD[Instance]
    Attributes
      protected
    Definition Classes
      ClassifierParams
33.  def extractInstances(dataset: Dataset[_], validateInstance: (Instance) ⇒ Unit): RDD[Instance]
    Attributes
      protected
    Definition Classes
      PredictorParams
34.  def extractInstances(dataset: Dataset[_]): RDD[Instance]
    Attributes
      protected
    Definition Classes
      PredictorParams
35.  def extractLabeledPoints(dataset: Dataset[_], numClasses: Int): RDD[org.apache.spark.ml.feature.LabeledPoint]
    Attributes
      protected
    Definition Classes
      Classifier
36.  def extractLabeledPoints(dataset: Dataset[_]): RDD[org.apache.spark.ml.feature.LabeledPoint]
    Attributes
      protected
    Definition Classes
      Predictor
37.  final def extractParamMap(): ParamMap
    Definition Classes
      Params
38.  final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
      Params
39.  final val featuresCol: Param[String]
    Definition Classes
      HasFeaturesCol
40.  def finalize(): Unit
    Attributes
      protected[java.lang]
    Definition Classes
      AnyRef
    Annotations
      @throws( classOf[java.lang.Throwable] )
41.  def fit(dataset: Dataset[_]): XGBoostClassificationModel
    Definition Classes
      Predictor → Estimator
42.  def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[XGBoostClassificationModel]
    Definition Classes
      Estimator
    Annotations
      @Since( "2.0.0" )
43.  def fit(dataset: Dataset[_], paramMap: ParamMap): XGBoostClassificationModel
    Definition Classes
      Estimator
    Annotations
      @Since( "2.0.0" )
44.  def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): XGBoostClassificationModel
    Definition Classes
      Estimator
    Annotations
      @Since( "2.0.0" ) @varargs()
45.  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
46.  final def get[T](param: Param[T]): Option[T]
    Definition Classes
      Params
47.  final def getAllowNonZeroForMissingValue: Boolean
    Definition Classes
      GeneralParams
48.  final def getAlpha: Double
    Definition Classes
      BoosterParams
49.  final def getBaseMarginCol: String

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

    Definition Classes
      HasContribPredictionCol
57.  final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
      Params
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 getFeaturesCol: String
    Definition Classes
      HasFeaturesCol
62.  final def getGamma: Double
    Definition Classes
      BoosterParams
63.  final def getGrowPolicy: String
    Definition Classes
      BoosterParams
64.  final def getInteractionConstraints: String
    Definition Classes
      BoosterParams
65.  final def getLabelCol: String
    Definition Classes
      HasLabelCol
66.  final def getLambda: Double
    Definition Classes
      BoosterParams
67.  final def getLambdaBias: Double
    Definition Classes
      BoosterParams
68.  final def getLeafPredictionCol: String

    Definition Classes
      HasLeafPredictionCol
69.  final def getMaxBins: Int
    Definition Classes
      BoosterParams
70.  final def getMaxDeltaStep: Double
    Definition Classes
      BoosterParams
71.  final def getMaxDepth: Int
    Definition Classes
      BoosterParams
72.  final def getMaxLeaves: Int
    Definition Classes
      BoosterParams
73.  final def getMaximizeEvaluationMetrics: Boolean
    Definition Classes
      LearningTaskParams
74.  final def getMinChildWeight: Double
    Definition Classes
      BoosterParams
75.  final def getMissing: Float
    Definition Classes
      GeneralParams
76.  final def getMonotoneConstraints: String
    Definition Classes
      BoosterParams
77.  final def getNormalizeType: String
    Definition Classes
      BoosterParams
78.  final def getNthread: Int
    Definition Classes
      GeneralParams
79.  final def getNumClass: Int

    Definition Classes
      HasNumClass
80.  def getNumClasses(dataset: Dataset[_], maxNumClasses: Int): Int
    Attributes
      protected
    Definition Classes
      Classifier
81.  final def getNumEarlyStoppingRounds: Int
    Definition Classes
      LearningTaskParams
82.  final def getNumRound: Int
    Definition Classes
      GeneralParams
83.  final def getNumWorkers: Int
    Definition Classes
      GeneralParams
84.  final def getObjective: String
    Definition Classes
      LearningTaskParams
85.  final def getObjectiveType: String
    Definition Classes
      LearningTaskParams
86.  final def getOrDefault[T](param: Param[T]): T
    Definition Classes
      Params
87.  def getParam(paramName: String): Param[Any]
    Definition Classes
      Params
88.  final def getPredictionCol: String
    Definition Classes
      HasPredictionCol
89.  final def getProbabilityCol: String
    Definition Classes
      HasProbabilityCol
90.  final def getRateDrop: Double
    Definition Classes
      BoosterParams
91.  final def getRawPredictionCol: String
    Definition Classes
      HasRawPredictionCol
92.  final def getSampleType: String
    Definition Classes
      BoosterParams
93.  final def getScalePosWeight: Double
    Definition Classes
      BoosterParams
94.  final def getSeed: Long
    Definition Classes
      GeneralParams
95.  final def getSilent: Int
    Definition Classes
      GeneralParams
96.  final def getSinglePrecisionHistogram: Boolean
    Definition Classes
      BoosterParams
97.  final def getSketchEps: Double
    Definition Classes
      BoosterParams
98.  final def getSkipDrop: Double
    Definition Classes
      BoosterParams
99.  final def getSubsample: Double
    Definition Classes
      BoosterParams
100.  def getThresholds: Array[Double]
     Definition Classes
       HasThresholds
101.  final def getTimeoutRequestWorkers: Long
     Definition Classes
       GeneralParams
102.  final def getTrainTestRatio: Double
     Definition Classes
       LearningTaskParams
103.  final def getTreeLimit: Int
     Definition Classes
       BoosterParams
104.  final def getTreeMethod: String
     Definition Classes
       BoosterParams
105.  final def getUseExternalMemory: Boolean
     Definition Classes
       GeneralParams
106.  final def getVerbosity: Int
     Definition Classes
       GeneralParams
107.  final def getWeightCol: String
     Definition Classes
       HasWeightCol
108.  final val growPolicy: Param[String]

     growth policy for fast histogram algorithm

     growth policy for fast histogram algorithm

     Definition Classes
       BoosterParams
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.  final def isInstanceOf[T0]: Boolean
     Definition Classes
       Any
117.  final def isSet(param: Param[_]): Boolean
     Definition Classes
       Params
118.  def isTraceEnabled(): Boolean
     Attributes
       protected
     Definition Classes
       Logging
119.  final val killSparkContextOnWorkerFailure: BooleanParam

     whether killing SparkContext when training task fails

     whether killing SparkContext when training task fails

     Definition Classes
       LearningTaskParams
120.  final val labelCol: Param[String]
     Definition Classes
       HasLabelCol
121.  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
122.  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
123.  final val leafPredictionCol: Param[String]

     Param for leaf prediction column name.

     Param for leaf prediction column name.

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

     maximum number of bins in histogram

     maximum number of bins in histogram

     Definition Classes
       BoosterParams
137.  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
138.  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
139.  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
140.  final val maximizeEvaluationMetrics: BooleanParam
     Definition Classes
       LearningTaskParams
141.  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
142.  final val missing: FloatParam

     the value treated as missing.

     the value treated as missing. default: Float.NaN

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

     number of threads used by per worker.

     number of threads used by per worker. default 1

     Definition Classes
       GeneralParams
150.  final val numClass: IntParam

     number of classes

     number of classes

     Definition Classes
       HasNumClass
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:pairwise, 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 val probabilityCol: Param[String]
     Definition Classes
       HasProbabilityCol
159.  final def rabitConnectRetry: IntParam
     Definition Classes
       RabitParams
160.  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
161.  final def rabitTimeout: IntParam
     Definition Classes
       RabitParams
162.  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
163.  final val rawPredictionCol: Param[String]
     Definition Classes
       HasRawPredictionCol
164.  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
165.  def save(path: String): Unit
     Definition Classes
       MLWritable
     Annotations
       @Since( "1.6.0" ) @throws( ... )
166.  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
167.  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
168.  final def set(paramPair: ParamPair[_]): XGBoostClassifier.this.type
     Attributes
       protected
     Definition Classes
       Params
169.  final def set(param: String, value: Any): XGBoostClassifier.this.type
     Attributes
       protected
     Definition Classes
       Params
170.  final def set[T](param: Param[T], value: T): XGBoostClassifier.this.type
     Definition Classes
       Params
171.  def setAllowNonZeroForMissing(value: Boolean): XGBoostClassifier.this.type
172.  def setAlpha(value: Double): XGBoostClassifier.this.type
173.  def setBaseMarginCol(value: String): XGBoostClassifier.this.type
174.  def setBaseScore(value: Double): XGBoostClassifier.this.type
175.  def setCheckpointInterval(value: Int): XGBoostClassifier.this.type
176.  def setCheckpointPath(value: String): XGBoostClassifier.this.type
177.  def setColsampleBylevel(value: Double): XGBoostClassifier.this.type
178.  def setColsampleBytree(value: Double): XGBoostClassifier.this.type
179.  def setCustomEval(value: EvalTrait): XGBoostClassifier.this.type
180.  def setCustomObj(value: ObjectiveTrait): XGBoostClassifier.this.type
181.  final def setDefault(paramPairs: ParamPair[_]*): XGBoostClassifier.this.type
     Attributes
       protected
     Definition Classes
       Params
182.  final def setDefault[T](param: Param[T], value: T): XGBoostClassifier.this.type
     Attributes
       protected
     Definition Classes
       Params
183.  def setEta(value: Double): XGBoostClassifier.this.type
184.  def setEvalMetric(value: String): XGBoostClassifier.this.type
185.  def setEvalSets(evalSets: Map[String, DataFrame]): XGBoostClassifier.this.type
     Definition Classes
       NonParamVariables
186.  def setFeaturesCol(value: String): XGBoostClassifier
     Definition Classes
       Predictor
187.  def setGamma(value: Double): XGBoostClassifier.this.type
188.  def setGrowPolicy(value: String): XGBoostClassifier.this.type
189.  def setLabelCol(value: String): XGBoostClassifier
     Definition Classes
       Predictor
190.  def setLambda(value: Double): XGBoostClassifier.this.type
191.  def setLambdaBias(value: Double): XGBoostClassifier.this.type
192.  def setMaxBins(value: Int): XGBoostClassifier.this.type
193.  def setMaxDeltaStep(value: Double): XGBoostClassifier.this.type
194.  def setMaxDepth(value: Int): XGBoostClassifier.this.type
195.  def setMaxLeaves(value: Int): XGBoostClassifier.this.type
196.  def setMaximizeEvaluationMetrics(value: Boolean): XGBoostClassifier.this.type
197.  def setMinChildWeight(value: Double): XGBoostClassifier.this.type
198.  def setMissing(value: Float): XGBoostClassifier.this.type
199.  def setNormalizeType(value: String): XGBoostClassifier.this.type
200.  def setNthread(value: Int): XGBoostClassifier.this.type
201.  def setNumClass(value: Int): XGBoostClassifier.this.type
202.  def setNumEarlyStoppingRounds(value: Int): XGBoostClassifier.this.type
203.  def setNumRound(value: Int): XGBoostClassifier.this.type
204.  def setNumWorkers(value: Int): XGBoostClassifier.this.type
205.  def setObjective(value: String): XGBoostClassifier.this.type
206.  def setObjectiveType(value: String): XGBoostClassifier.this.type
207.  def setPredictionCol(value: String): XGBoostClassifier
     Definition Classes
       Predictor
208.  def setProbabilityCol(value: String): XGBoostClassifier
     Definition Classes
       ProbabilisticClassifier
209.  def setRateDrop(value: Double): XGBoostClassifier.this.type
210.  def setRawPredictionCol(value: String): XGBoostClassifier
     Definition Classes
       Classifier
211.  def setSampleType(value: String): XGBoostClassifier.this.type
212.  def setScalePosWeight(value: Double): XGBoostClassifier.this.type
213.  def setSeed(value: Long): XGBoostClassifier.this.type
214.  def setSilent(value: Int): XGBoostClassifier.this.type
215.  def setSinglePrecisionHistogram(value: Boolean): XGBoostClassifier.this.type
216.  def setSketchEps(value: Double): XGBoostClassifier.this.type
217.  def setSkipDrop(value: Double): XGBoostClassifier.this.type
218.  def setSubsample(value: Double): XGBoostClassifier.this.type
219.  def setThresholds(value: Array[Double]): XGBoostClassifier
     Definition Classes
       ProbabilisticClassifier
220.  def setTimeoutRequestWorkers(value: Long): XGBoostClassifier.this.type
221.  def setTrainTestRatio(value: Double): XGBoostClassifier.this.type
222.  def setTreeMethod(value: String): XGBoostClassifier.this.type
223.  def setUseExternalMemory(value: Boolean): XGBoostClassifier.this.type
224.  def setWeightCol(value: String): XGBoostClassifier.this.type
225.  final val silent: IntParam

     Deprecated.

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

     Definition Classes
       GeneralParams
226.  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
227.  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
228.  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
229.  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
230.  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
231.  final def synchronized[T0](arg0: ⇒ T0): T0
     Definition Classes
       AnyRef
232.  val thresholds: DoubleArrayParam
     Definition Classes
       HasThresholds
233.  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
234.  def toString(): String
     Definition Classes
       Identifiable → AnyRef → Any
235.  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
236.  def train(dataset: Dataset[_]): XGBoostClassificationModel
     Attributes
       protected
     Definition Classes
       XGBoostClassifier → Predictor
237.  final val trainTestRatio: DoubleParam

     Fraction of training points to use for testing.

     Fraction of training points to use for testing.

     Definition Classes
       LearningTaskParams
238.  def transformSchema(schema: StructType): StructType
     Definition Classes
       Predictor → PipelineStage
239.  def transformSchema(schema: StructType, logging: Boolean): StructType
     Attributes
       protected
     Definition Classes
       PipelineStage
     Annotations
       @DeveloperApi()
240.  final val treeLimit: IntParam
     Definition Classes
       BoosterParams
241.  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
242.  val uid: String
     Definition Classes
       XGBoostClassifier → Identifiable
243.  final val useExternalMemory: BooleanParam

     whether to use external memory as cache.

     whether to use external memory as cache. default: false

     Definition Classes
       GeneralParams
244.  def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
     Attributes
       protected
     Definition Classes
       ProbabilisticClassifierParams → ClassifierParams → PredictorParams
245.  def validateLabel(label: Double, numClasses: Int): Unit
     Attributes
       protected
     Definition Classes
       Classifier
246.  def validateNumClasses(numClasses: Int): Unit
     Attributes
       protected
     Definition Classes
       Classifier
247.  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
248.  final def wait(): Unit
     Definition Classes
       AnyRef
     Annotations
       @throws( ... )
249.  final def wait(arg0: Long, arg1: Int): Unit
     Definition Classes
       AnyRef
     Annotations
       @throws( ... )
250.  final def wait(arg0: Long): Unit
     Definition Classes
       AnyRef
     Annotations
       @native() @throws( ... )
251.  final val weightCol: Param[String]
     Definition Classes
       HasWeightCol
252.  def write: MLWriter
     Definition Classes
       DefaultParamsWritable → MLWritable

### Inherited from DefaultParamsWritable

### Inherited from MLWritable

### Inherited from XGBoostClassifierParams

### 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 HasNumClass

### Inherited from HasBaseMarginCol

### Inherited from HasWeightCol

### Inherited from ProbabilisticClassifier[Vector, XGBoostClassifier, XGBoostClassificationModel]

### Inherited from ProbabilisticClassifierParams

### Inherited from HasThresholds

### Inherited from HasProbabilityCol

### Inherited from Classifier[Vector, XGBoostClassifier, XGBoostClassificationModel]

### Inherited from ClassifierParams

### Inherited from HasRawPredictionCol

### Inherited from Predictor[Vector, XGBoostClassifier, XGBoostClassificationModel]

### Inherited from PredictorParams

### Inherited from HasPredictionCol

### Inherited from HasFeaturesCol

### Inherited from HasLabelCol

### Inherited from Estimator[XGBoostClassificationModel]

### Inherited from PipelineStage

### Inherited from Logging

### Inherited from Params

### Inherited from Serializable

### Inherited from Identifiable

### Inherited from AnyRef

### Inherited from Any

### getParam

### param

### Ungrouped