xgboost4j-spark_2.12 1.6.2 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
- IteratorWrapper
- PreXGBoost
- TrackerConf
- Watches
- 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, HasNumClass, XGBoostEstimatorCommon, HasHandleInvalid, HasFeaturesCols, HasContribPredictionCol, HasLeafPredictionCol, HasBaseMarginCol, HasWeightCol, NonParamVariables, ParamMapFuncs, RabitParams, BoosterParams, LearningTaskParams, GeneralParams, 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. HasNumClass
6. XGBoostEstimatorCommon
7. HasHandleInvalid
8. HasFeaturesCols
9. HasContribPredictionCol
10. HasLeafPredictionCol
11. HasBaseMarginCol
12. HasWeightCol
13. NonParamVariables
14. ParamMapFuncs
15. RabitParams
16. BoosterParams
17. LearningTaskParams
18. GeneralParams
19. ProbabilisticClassificationModel
20. ProbabilisticClassifierParams
21. HasThresholds
22. HasProbabilityCol
23. ClassificationModel
24. ClassifierParams
25. HasRawPredictionCol
26. PredictionModel
27. PredictorParams
28. HasPredictionCol
29. HasFeaturesCol
30. HasLabelCol
31. Model
32. Transformer
33. PipelineStage
34. Logging
35. Params
36. Serializable
37. Serializable
38. Identifiable
39. AnyRef
40. 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.  final val featuresCols: StringArrayParam

    Param for the names of feature columns.

    Param for the names of feature columns.

    Definition Classes
      HasFeaturesCols
39.  def featuresDataType: DataType
    Attributes
      protected
    Definition Classes
      PredictionModel
40.  def finalize(): Unit
    Attributes
      protected[java.lang]
    Definition Classes
      AnyRef
    Annotations
      @throws( classOf[java.lang.Throwable] )
41.  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
42.  final def get[T](param: Param[T]): Option[T]
    Definition Classes
      Params
43.  final def getAllowNonZeroForMissingValue: Boolean
    Definition Classes
      GeneralParams
44.  final def getAlpha: Double
    Definition Classes
      BoosterParams
45.  final def getBaseMarginCol: String

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

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

    Definition Classes
      HasFeaturesCols
59.  final def getGamma: Double
    Definition Classes
      BoosterParams
60.  final def getGrowPolicy: String
    Definition Classes
      BoosterParams
61.  final def getHandleInvalid: String
    Definition Classes
      HasHandleInvalid
62.  final def getInferBatchSize: Int

    Definition Classes
      InferenceParams
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 getNumClass: Int

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

     growth policy for fast histogram algorithm

     growth policy for fast histogram algorithm

     Definition Classes
       BoosterParams
107.  val handleInvalid: Param[String]

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

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

     Definition Classes
       XGBoostEstimatorCommon → HasHandleInvalid
108.  final def hasDefault[T](param: Param[T]): Boolean
     Definition Classes
       Params
109.  def hasParam(paramName: String): Boolean
     Definition Classes
       Params
110.  def hasParent: Boolean
     Definition Classes
       Model
111.  def hashCode(): Int
     Definition Classes
       AnyRef → Any
     Annotations
       @native()
112.  final val inferBatchSize: IntParam

     batch size of inference iteration

     batch size of inference iteration

     Definition Classes
       InferenceParams
113.  def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
     Attributes
       protected
     Definition Classes
       Logging
114.  def initializeLogIfNecessary(isInterpreter: Boolean): Unit
     Attributes
       protected
     Definition Classes
       Logging
115.  final val interactionConstraints: Param[String]
     Definition Classes
       BoosterParams
116.  final def isDefined(param: Param[_]): Boolean
     Definition Classes
       Params
117.  def isFeaturesColSet(schema: StructType): Boolean

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

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

     Definition Classes
       XGBoostEstimatorCommon
118.  def isFeaturesColsValid: Boolean

     Check if featuresCols is valid

     Check if featuresCols is valid

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

     Param for leaf prediction column name.

     Param for leaf prediction column name.

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

     maximum number of bins in histogram

     maximum number of bins in histogram

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

     the value treated as missing.

     the value treated as missing. default: Float.NaN

     Definition Classes
       GeneralParams
145.  final val monotoneConstraints: Param[String]
     Definition Classes
       BoosterParams
146.  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".

147.  final def ne(arg0: AnyRef): Boolean
     Definition Classes
       AnyRef
148.  def needDeterministicRepartitioning: Boolean
     Definition Classes
       XGBoostEstimatorCommon
149.  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
150.  final def notify(): Unit
     Definition Classes
       AnyRef
     Annotations
       @native()
151.  final def notifyAll(): Unit
     Definition Classes
       AnyRef
     Annotations
       @native()
152.  final val nthread: IntParam

     number of threads used by per worker.

     number of threads used by per worker. default 1

     Definition Classes
       GeneralParams
153.  final val numClass: IntParam

     number of classes

     number of classes

     Definition Classes
       HasNumClass
154.  val numClasses: Int
     Definition Classes
       XGBoostClassificationModel → ClassificationModel
155.  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
156.  def numFeatures: Int
     Definition Classes
       PredictionModel
     Annotations
       @Since( "1.6.0" )
157.  final val numRound: IntParam

     The number of rounds for boosting

     The number of rounds for boosting

     Definition Classes
       GeneralParams
158.  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
159.  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
160.  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
161.  lazy val params: Array[Param[_]]
     Definition Classes
       Params
162.  var parent: Estimator[XGBoostClassificationModel]
     Definition Classes
       Model
163.  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
164.  def predictProbability(features: Vector): Vector
     Definition Classes
       ProbabilisticClassificationModel
     Annotations
       @Since( "3.0.0" )
165.  def predictRaw(features: Vector): Vector
     Definition Classes
       XGBoostClassificationModel → ClassificationModel
166.  final val predictionCol: Param[String]
     Definition Classes
       HasPredictionCol
167.  def probability2prediction(probability: Vector): Double
     Attributes
       protected
     Definition Classes
       ProbabilisticClassificationModel
168.  final val probabilityCol: Param[String]
     Definition Classes
       HasProbabilityCol
169.  final def rabitConnectRetry: IntParam
     Definition Classes
       RabitParams
170.  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
171.  final def rabitTimeout: IntParam
     Definition Classes
       RabitParams
172.  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
173.  def raw2prediction(rawPrediction: Vector): Double
     Attributes
       protected
     Definition Classes
       ProbabilisticClassificationModel → ClassificationModel
174.  def raw2probability(rawPrediction: Vector): Vector
     Attributes
       protected
     Definition Classes
       ProbabilisticClassificationModel
175.  def raw2probabilityInPlace(rawPrediction: Vector): Vector
     Attributes
       protected
     Definition Classes
       XGBoostClassificationModel → ProbabilisticClassificationModel
176.  final val rawPredictionCol: Param[String]
     Definition Classes
       HasRawPredictionCol
177.  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
178.  def save(path: String): Unit
     Definition Classes
       MLWritable
     Annotations
       @Since( "1.6.0" ) @throws( ... )
179.  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
180.  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
181.  final def set(paramPair: ParamPair[_]): XGBoostClassificationModel.this.type
     Attributes
       protected
     Definition Classes
       Params
182.  final def set(param: String, value: Any): XGBoostClassificationModel.this.type
     Attributes
       protected
     Definition Classes
       Params
183.  final def set[T](param: Param[T], value: T): XGBoostClassificationModel.this.type
     Definition Classes
       Params
184.  def setAllowNonZeroForMissing(value: Boolean): XGBoostClassificationModel.this.type
185.  def setContribPredictionCol(value: String): XGBoostClassificationModel.this.type
186.  final def setDefault(paramPairs: ParamPair[_]*): XGBoostClassificationModel.this.type
     Attributes
       protected
     Definition Classes
       Params
187.  final def setDefault[T](param: Param[T], value: T): XGBoostClassificationModel.this.type
     Attributes
       protected
     Definition Classes
       Params
188.  def setEvalSets(evalSets: Map[String, DataFrame]): XGBoostClassificationModel.this.type
     Definition Classes
       NonParamVariables
189.  def setFeaturesCol(value: Array[String]): XGBoostClassificationModel.this.type

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

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

     Definition Classes
       XGBoostEstimatorCommon
190.  def setFeaturesCol(value: String): XGBoostClassificationModel
     Definition Classes
       PredictionModel
191.  def setHandleInvalid(value: String): XGBoostClassificationModel.this.type

     Set the handleInvalid for VectorAssembler

     Set the handleInvalid for VectorAssembler

     Definition Classes
       XGBoostEstimatorCommon
192.  def setInferBatchSize(value: Int): XGBoostClassificationModel.this.type
193.  def setLeafPredictionCol(value: String): XGBoostClassificationModel.this.type
194.  def setMissing(value: Float): XGBoostClassificationModel.this.type
195.  def setParent(parent: Estimator[XGBoostClassificationModel]): XGBoostClassificationModel
     Definition Classes
       Model
196.  def setPredictionCol(value: String): XGBoostClassificationModel
     Definition Classes
       PredictionModel
197.  def setProbabilityCol(value: String): XGBoostClassificationModel
     Definition Classes
       ProbabilisticClassificationModel
198.  def setRawPredictionCol(value: String): XGBoostClassificationModel
     Definition Classes
       ClassificationModel
199.  def setThresholds(value: Array[Double]): XGBoostClassificationModel
     Definition Classes
       ProbabilisticClassificationModel
200.  def setTreeLimit(value: Int): XGBoostClassificationModel.this.type
201.  final val silent: IntParam

     Deprecated.

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

     Definition Classes
       GeneralParams
202.  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
203.  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
204.  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
205.  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
206.  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
207.  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.

208.  final def synchronized[T0](arg0: ⇒ T0): T0
     Definition Classes
       AnyRef
209.  val thresholds: DoubleArrayParam
     Definition Classes
       HasThresholds
210.  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
211.  def toString(): String
     Definition Classes
       Identifiable → AnyRef → Any
212.  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
213.  final val trainTestRatio: DoubleParam

     Fraction of training points to use for testing.

     Fraction of training points to use for testing.

     Definition Classes
       LearningTaskParams
214.  def transform(dataset: Dataset[_]): DataFrame
     Definition Classes
       XGBoostClassificationModel → ProbabilisticClassificationModel → ClassificationModel → PredictionModel → Transformer
215.  def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
     Definition Classes
       Transformer
     Annotations
       @Since( "2.0.0" )
216.  def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
     Definition Classes
       Transformer
     Annotations
       @Since( "2.0.0" ) @varargs()
217.  final def transformImpl(dataset: Dataset[_]): DataFrame
     Definition Classes
       ClassificationModel → PredictionModel
218.  def transformSchema(schema: StructType): StructType
     Definition Classes
       XGBoostClassificationModel → ProbabilisticClassificationModel → ClassificationModel → PredictionModel → PipelineStage
219.  def transformSchema(schema: StructType, logging: Boolean): StructType
     Attributes
       protected
     Definition Classes
       PipelineStage
     Annotations
       @DeveloperApi()
220.  def transformSchemaWithFeaturesCols(fit: Boolean, schema: StructType): StructType

     check the features columns type

     check the features columns type

     Definition Classes
       XGBoostEstimatorCommon
221.  final val treeLimit: IntParam
     Definition Classes
       BoosterParams
222.  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
223.  val uid: String
     Definition Classes
       XGBoostClassificationModel → Identifiable
224.  final val useExternalMemory: BooleanParam

     whether to use external memory as cache.

     whether to use external memory as cache. default: false

     Definition Classes
       GeneralParams
225.  def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
     Attributes
       protected
     Definition Classes
       ProbabilisticClassifierParams → ClassifierParams → PredictorParams
226.  def vectorize(input: Dataset[_]): (Dataset[_], String)

     Vectorize the features columns if necessary.

     Vectorize the features columns if necessary.

     input

       the input dataset

     returns

       (output dataset and the feature column name)

     Definition Classes
       XGBoostEstimatorCommon
227.  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
228.  final def wait(): Unit
     Definition Classes
       AnyRef
     Annotations
       @throws( ... )
229.  final def wait(arg0: Long, arg1: Int): Unit
     Definition Classes
       AnyRef
     Annotations
       @throws( ... )
230.  final def wait(arg0: Long): Unit
     Definition Classes
       AnyRef
     Annotations
       @native() @throws( ... )
231.  final val weightCol: Param[String]
     Definition Classes
       HasWeightCol
232.  def write: MLWriter
     Definition Classes
       XGBoostClassificationModel → MLWritable

### Inherited from MLWritable

### Inherited from InferenceParams

### Inherited from XGBoostClassifierParams

### Inherited from HasNumClass

### Inherited from XGBoostEstimatorCommon

### Inherited from HasHandleInvalid

### Inherited from HasFeaturesCols

### Inherited from HasContribPredictionCol

### Inherited from HasLeafPredictionCol

### Inherited from HasBaseMarginCol

### Inherited from HasWeightCol

### Inherited from NonParamVariables

### Inherited from ParamMapFuncs

### Inherited from RabitParams

### Inherited from BoosterParams

### Inherited from LearningTaskParams

### Inherited from GeneralParams

### Inherited from 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