xgboost4j-spark_2.12 1.4.1 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

# XGBoostClassificationModel 

###
Companion object XGBoostClassificationModel

#### class XGBoostClassificationModel extends ProbabilisticClassificationModel[Vector, XGBoostClassificationModel] with XGBoostClassifierParams with InferenceParams with MLWritable with Serializable

Linear Supertypes
MLWritable, InferenceParams, XGBoostClassifierParams, XGBoostEstimatorCommon, NonParamVariables, ParamMapFuncs, RabitParams, BoosterParams, LearningTaskParams, GeneralParams, HasContribPredictionCol, HasLeafPredictionCol, HasNumClass, HasBaseMarginCol, HasWeightCol, ProbabilisticClassificationModel[Vector, XGBoostClassificationModel], ProbabilisticClassifierParams, HasThresholds, HasProbabilityCol, ClassificationModel[Vector, XGBoostClassificationModel], ClassifierParams, HasRawPredictionCol, PredictionModel[Vector, XGBoostClassificationModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Model[XGBoostClassificationModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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2. By Inheritance

Inherited

1. XGBoostClassificationModel
2. MLWritable
3. InferenceParams
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. ProbabilisticClassificationModel
18. ProbabilisticClassifierParams
19. HasThresholds
20. HasProbabilityCol
21. ClassificationModel
22. ClassifierParams
23. HasRawPredictionCol
24. PredictionModel
25. PredictorParams
26. HasPredictionCol
27. HasFeaturesCol
28. HasLabelCol
29. Model
30. Transformer
31. PipelineStage
32. Logging
33. Params
34. Serializable
35. Serializable
36. Identifiable
37. AnyRef
38. Any

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Visibility

1. Public
2. All

### Instance Constructors

1.  new XGBoostClassificationModel(uid: String)

### Value Members

1.  final def !=(arg0: Any): Boolean
   Definition Classes
     AnyRef → Any
2.  final def ##(): Int
   Definition Classes
     AnyRef → Any
3.  final def $[T](param: Param[T]): T
   Attributes
     protected
   Definition Classes
     Params
4.  final def ==(arg0: Any): Boolean
   Definition Classes
     AnyRef → Any
5.  def MLlib2XGBoostParams: Map[String, Any]
   Definition Classes
     ParamMapFuncs
6.  def 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[_]): XGBoostClassificationModel.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): XGBoostClassificationModel
    Definition Classes
      XGBoostClassificationModel → Model → Transformer → 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.  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 featuresDataType: DataType
    Attributes
      protected
    Definition Classes
      PredictionModel
39.  def finalize(): Unit
    Attributes
      protected[java.lang]
    Definition Classes
      AnyRef
    Annotations
      @throws( classOf[java.lang.Throwable] )
40.  final val gamma: DoubleParam

    minimum loss reduction required to make a further partition on a leaf node of the tree.

    minimum loss reduction required to make a further partition on a leaf node of the tree. the larger, the more conservative the algorithm will be. [default=0] range: [0, Double.MaxValue]

    Definition Classes
      BoosterParams
41.  final def get[T](param: Param[T]): Option[T]
    Definition Classes
      Params
42.  final def getAllowNonZeroForMissingValue: Boolean
    Definition Classes
      GeneralParams
43.  final def getAlpha: Double
    Definition Classes
      BoosterParams
44.  final def getBaseMarginCol: String

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

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

    Definition Classes
      InferenceParams
60.  final def getInteractionConstraints: String
    Definition Classes
      BoosterParams
61.  final def getLabelCol: String
    Definition Classes
      HasLabelCol
62.  final def getLambda: Double
    Definition Classes
      BoosterParams
63.  final def getLambdaBias: Double
    Definition Classes
      BoosterParams
64.  final def getLeafPredictionCol: String

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

    Definition Classes
      HasNumClass
76.  final def getNumEarlyStoppingRounds: Int
    Definition Classes
      LearningTaskParams
77.  final def getNumRound: Int
    Definition Classes
      GeneralParams
78.  final def getNumWorkers: Int
    Definition Classes
      GeneralParams
79.  final def getObjective: String
    Definition Classes
      LearningTaskParams
80.  final def getObjectiveType: String
    Definition Classes
      LearningTaskParams
81.  final def getOrDefault[T](param: Param[T]): T
    Definition Classes
      Params
82.  def getParam(paramName: String): Param[Any]
    Definition Classes
      Params
83.  final def getPredictionCol: String
    Definition Classes
      HasPredictionCol
84.  final def getProbabilityCol: String
    Definition Classes
      HasProbabilityCol
85.  final def getRateDrop: Double
    Definition Classes
      BoosterParams
86.  final def getRawPredictionCol: String
    Definition Classes
      HasRawPredictionCol
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.  def getThresholds: Array[Double]
    Definition Classes
      HasThresholds
96.  final def getTimeoutRequestWorkers: Long
    Definition Classes
      GeneralParams
97.  final def getTrainTestRatio: Double
    Definition Classes
      LearningTaskParams
98.  final def getTreeLimit: Int
    Definition Classes
      BoosterParams
99.  final def getTreeMethod: String
    Definition Classes
      BoosterParams
100.  final def getUseExternalMemory: Boolean
     Definition Classes
       GeneralParams
101.  final def getVerbosity: Int
     Definition Classes
       GeneralParams
102.  final def getWeightCol: String
     Definition Classes
       HasWeightCol
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 hasParent: Boolean
     Definition Classes
       Model
107.  def hashCode(): Int
     Definition Classes
       AnyRef → Any
     Annotations
       @native()
108.  final val inferBatchSize: IntParam

     batch size of inference iteration

     batch size of inference iteration

     Definition Classes
       InferenceParams
109.  def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
     Attributes
       protected
     Definition Classes
       Logging
110.  def initializeLogIfNecessary(isInterpreter: Boolean): Unit
     Attributes
       protected
     Definition Classes
       Logging
111.  final val interactionConstraints: Param[String]
     Definition Classes
       BoosterParams
112.  final def isDefined(param: Param[_]): Boolean
     Definition Classes
       Params
113.  final def isInstanceOf[T0]: Boolean
     Definition Classes
       Any
114.  final def isSet(param: Param[_]): Boolean
     Definition Classes
       Params
115.  def isTraceEnabled(): Boolean
     Attributes
       protected
     Definition Classes
       Logging
116.  final val killSparkContextOnWorkerFailure: BooleanParam

     whether killing SparkContext when training task fails

     whether killing SparkContext when training task fails

     Definition Classes
       LearningTaskParams
117.  final val labelCol: Param[String]
     Definition Classes
       HasLabelCol
118.  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
119.  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
120.  final val leafPredictionCol: Param[String]

     Param for leaf prediction column name.

     Param for leaf prediction column name.

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

     maximum number of bins in histogram

     maximum number of bins in histogram

     Definition Classes
       BoosterParams
134.  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
135.  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
136.  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
137.  final val maximizeEvaluationMetrics: BooleanParam
     Definition Classes
       LearningTaskParams
138.  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
139.  final val missing: FloatParam

     the value treated as missing.

     the value treated as missing. default: Float.NaN

     Definition Classes
       GeneralParams
140.  final val monotoneConstraints: Param[String]
     Definition Classes
       BoosterParams
141.  def nativeBooster: Booster

     Get the native booster instance of this model.

     Get the native booster instance of this model. This is used to call low-level APIs on native booster, such as "getFeatureScore".

142.  final def ne(arg0: AnyRef): Boolean
     Definition Classes
       AnyRef
143.  def needDeterministicRepartitioning: Boolean
     Definition Classes
       XGBoostEstimatorCommon
144.  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
145.  final def notify(): Unit
     Definition Classes
       AnyRef
     Annotations
       @native()
146.  final def notifyAll(): Unit
     Definition Classes
       AnyRef
     Annotations
       @native()
147.  final val nthread: IntParam

     number of threads used by per worker.

     number of threads used by per worker. default 1

     Definition Classes
       GeneralParams
148.  final val numClass: IntParam

     number of classes

     number of classes

     Definition Classes
       HasNumClass
149.  val numClasses: Int
     Definition Classes
       XGBoostClassificationModel → ClassificationModel
150.  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
151.  def numFeatures: Int
     Definition Classes
       PredictionModel
     Annotations
       @Since( "1.6.0" )
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.  var parent: Estimator[XGBoostClassificationModel]
     Definition Classes
       Model
158.  def predict(features: Vector): Double

     Single instance prediction.

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

     Definition Classes
       XGBoostClassificationModel → ClassificationModel → PredictionModel
159.  def predictProbability(features: Vector): Vector
     Definition Classes
       ProbabilisticClassificationModel
     Annotations
       @Since( "3.0.0" )
160.  def predictRaw(features: Vector): Vector
     Definition Classes
       XGBoostClassificationModel → ClassificationModel
161.  final val predictionCol: Param[String]
     Definition Classes
       HasPredictionCol
162.  def probability2prediction(probability: Vector): Double
     Attributes
       protected
     Definition Classes
       ProbabilisticClassificationModel
163.  final val probabilityCol: Param[String]
     Definition Classes
       HasProbabilityCol
164.  final def rabitConnectRetry: IntParam
     Definition Classes
       RabitParams
165.  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
166.  final def rabitTimeout: IntParam
     Definition Classes
       RabitParams
167.  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
168.  def raw2prediction(rawPrediction: Vector): Double
     Attributes
       protected
     Definition Classes
       ProbabilisticClassificationModel → ClassificationModel
169.  def raw2probability(rawPrediction: Vector): Vector
     Attributes
       protected
     Definition Classes
       ProbabilisticClassificationModel
170.  def raw2probabilityInPlace(rawPrediction: Vector): Vector
     Attributes
       protected
     Definition Classes
       XGBoostClassificationModel → ProbabilisticClassificationModel
171.  final val rawPredictionCol: Param[String]
     Definition Classes
       HasRawPredictionCol
172.  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
173.  def save(path: String): Unit
     Definition Classes
       MLWritable
     Annotations
       @Since( "1.6.0" ) @throws( ... )
174.  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
175.  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
176.  final def set(paramPair: ParamPair[_]): XGBoostClassificationModel.this.type
     Attributes
       protected
     Definition Classes
       Params
177.  final def set(param: String, value: Any): XGBoostClassificationModel.this.type
     Attributes
       protected
     Definition Classes
       Params
178.  final def set[T](param: Param[T], value: T): XGBoostClassificationModel.this.type
     Definition Classes
       Params
179.  def setAllowNonZeroForMissing(value: Boolean): XGBoostClassificationModel.this.type
180.  def setContribPredictionCol(value: String): XGBoostClassificationModel.this.type
181.  final def setDefault(paramPairs: ParamPair[_]*): XGBoostClassificationModel.this.type
     Attributes
       protected
     Definition Classes
       Params
182.  final def setDefault[T](param: Param[T], value: T): XGBoostClassificationModel.this.type
     Attributes
       protected
     Definition Classes
       Params
183.  def setEvalSets(evalSets: Map[String, DataFrame]): XGBoostClassificationModel.this.type
     Definition Classes
       NonParamVariables
184.  def setFeaturesCol(value: String): XGBoostClassificationModel
     Definition Classes
       PredictionModel
185.  def setInferBatchSize(value: Int): XGBoostClassificationModel.this.type
186.  def setLeafPredictionCol(value: String): XGBoostClassificationModel.this.type
187.  def setMissing(value: Float): XGBoostClassificationModel.this.type
188.  def setParent(parent: Estimator[XGBoostClassificationModel]): XGBoostClassificationModel
     Definition Classes
       Model
189.  def setPredictionCol(value: String): XGBoostClassificationModel
     Definition Classes
       PredictionModel
190.  def setProbabilityCol(value: String): XGBoostClassificationModel
     Definition Classes
       ProbabilisticClassificationModel
191.  def setRawPredictionCol(value: String): XGBoostClassificationModel
     Definition Classes
       ClassificationModel
192.  def setThresholds(value: Array[Double]): XGBoostClassificationModel
     Definition Classes
       ProbabilisticClassificationModel
193.  def setTreeLimit(value: Int): XGBoostClassificationModel.this.type
194.  final val silent: IntParam

     Deprecated.

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

     Definition Classes
       GeneralParams
195.  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
196.  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
197.  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
198.  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
199.  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
200.  def summary: XGBoostTrainingSummary

     Returns summary (e.g.

     Returns summary (e.g. train/test objective history) of model on the training set. An exception is thrown if no summary is available.

201.  final def synchronized[T0](arg0: ⇒ T0): T0
     Definition Classes
       AnyRef
202.  val thresholds: DoubleArrayParam
     Definition Classes
       HasThresholds
203.  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
204.  def toString(): String
     Definition Classes
       Identifiable → AnyRef → Any
205.  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
206.  final val trainTestRatio: DoubleParam

     Fraction of training points to use for testing.

     Fraction of training points to use for testing.

     Definition Classes
       LearningTaskParams
207.  def transform(dataset: Dataset[_]): DataFrame
     Definition Classes
       XGBoostClassificationModel → ProbabilisticClassificationModel → ClassificationModel → PredictionModel → Transformer
208.  def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
     Definition Classes
       Transformer
     Annotations
       @Since( "2.0.0" )
209.  def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
     Definition Classes
       Transformer
     Annotations
       @Since( "2.0.0" ) @varargs()
210.  final def transformImpl(dataset: Dataset[_]): DataFrame
     Definition Classes
       ClassificationModel → PredictionModel
211.  def transformSchema(schema: StructType): StructType
     Definition Classes
       ProbabilisticClassificationModel → ClassificationModel → PredictionModel → PipelineStage
212.  def transformSchema(schema: StructType, logging: Boolean): StructType
     Attributes
       protected
     Definition Classes
       PipelineStage
     Annotations
       @DeveloperApi()
213.  final val treeLimit: IntParam
     Definition Classes
       BoosterParams
214.  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
215.  val uid: String
     Definition Classes
       XGBoostClassificationModel → Identifiable
216.  final val useExternalMemory: BooleanParam

     whether to use external memory as cache.

     whether to use external memory as cache. default: false

     Definition Classes
       GeneralParams
217.  def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
     Attributes
       protected
     Definition Classes
       ProbabilisticClassifierParams → ClassifierParams → PredictorParams
218.  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
219.  final def wait(): Unit
     Definition Classes
       AnyRef
     Annotations
       @throws( ... )
220.  final def wait(arg0: Long, arg1: Int): Unit
     Definition Classes
       AnyRef
     Annotations
       @throws( ... )
221.  final def wait(arg0: Long): Unit
     Definition Classes
       AnyRef
     Annotations
       @native() @throws( ... )
222.  final val weightCol: Param[String]
     Definition Classes
       HasWeightCol
223.  def write: MLWriter
     Definition Classes
       XGBoostClassificationModel → MLWritable

### Inherited from MLWritable

### Inherited from InferenceParams

### 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 ProbabilisticClassificationModel[Vector, XGBoostClassificationModel]

### Inherited from ProbabilisticClassifierParams

### Inherited from HasThresholds

### Inherited from HasProbabilityCol

### Inherited from ClassificationModel[Vector, XGBoostClassificationModel]

### Inherited from ClassifierParams

### Inherited from HasRawPredictionCol

### Inherited from PredictionModel[Vector, XGBoostClassificationModel]

### Inherited from PredictorParams

### Inherited from HasPredictionCol

### Inherited from HasFeaturesCol

### Inherited from HasLabelCol

### Inherited from Model[XGBoostClassificationModel]

### Inherited from Transformer

### Inherited from PipelineStage

### Inherited from Logging

### Inherited from Params

### Inherited from Serializable

### Inherited from Identifiable

### Inherited from AnyRef

### Inherited from Any

### getParam

### param

### Ungrouped