Packages

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

Linear Supertypes
DefaultParamsWritable, MLWritable, XGBoostClassifierParams, HasNumClass, XGBoostEstimatorCommon, HasHandleInvalid, HasFeaturesCols, HasContribPredictionCol, HasLeafPredictionCol, HasBaseMarginCol, HasWeightCol, NonParamVariables, ParamMapFuncs, RabitParams, BoosterParams, LearningTaskParams, GeneralParams, 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. 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. ProbabilisticClassifier
  20. ProbabilisticClassifierParams
  21. HasThresholds
  22. HasProbabilityCol
  23. Classifier
  24. ClassifierParams
  25. HasRawPredictionCol
  26. Predictor
  27. PredictorParams
  28. HasPredictionCol
  29. HasFeaturesCol
  30. HasLabelCol
  31. Estimator
  32. PipelineStage
  33. Logging
  34. Params
  35. Serializable
  36. Serializable
  37. Identifiable
  38. AnyRef
  39. 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[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  17. final val colsampleBylevel: DoubleParam

    subsample ratio of columns for each split, in each level.

    subsample ratio of columns for each split, in each level. [default=1] range: (0,1]

    Definition Classes
    BoosterParams
  18. final val colsampleBytree: DoubleParam

    subsample ratio of columns when constructing each tree.

    subsample ratio of columns when constructing each tree. [default=1] range: (0,1]

    Definition Classes
    BoosterParams
  19. final val contribPredictionCol: Param[String]

    Param for contribution prediction column name.

    Param for contribution prediction column name.

    Definition Classes
    HasContribPredictionCol
  20. def copy(extra: ParamMap): 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 val device: Param[String]

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

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

    Definition Classes
    BoosterParams
  26. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  27. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  28. final val eta: DoubleParam

    step size shrinkage used in update to prevents overfitting.

    step size shrinkage used in update to prevents overfitting. After each boosting step, we can directly get the weights of new features and eta actually shrinks the feature weights to make the boosting process more conservative. [default=0.3] range: [0,1]

    Definition Classes
    BoosterParams
  29. final val evalMetric: Param[String]

    evaluation metrics for validation data, a default metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking).

    evaluation metrics for validation data, a default metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking). options: rmse, rmsle, mae, mape, logloss, error, merror, mlogloss, auc, aucpr, ndcg, map, gamma-deviance

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

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

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

    Definition Classes
    GeneralParams
  36. final val featureTypes: StringArrayParam

    Feature types, q is numeric and c is categorical.

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

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

    Param for the names of feature columns.

    Param for the names of feature columns.

    Definition Classes
    HasFeaturesCols
  39. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  40. def fit(dataset: Dataset[_]): XGBoostClassificationModel
    Definition Classes
    Predictor → Estimator
  41. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[XGBoostClassificationModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  42. def fit(dataset: Dataset[_], paramMap: ParamMap): XGBoostClassificationModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  43. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): XGBoostClassificationModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  44. final val gamma: DoubleParam

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

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

    Definition Classes
    BoosterParams
  45. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  46. final def getAllowNonZeroForMissingValue: Boolean
    Definition Classes
    GeneralParams
  47. final def getAlpha: Double
    Definition Classes
    BoosterParams
  48. final def getBaseMarginCol: String

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

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

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

    Definition Classes
    HasLeafPredictionCol
  73. final def getMaxBins: Int
    Definition Classes
    BoosterParams
  74. final def getMaxDeltaStep: Double
    Definition Classes
    BoosterParams
  75. final def getMaxDepth: Int
    Definition Classes
    BoosterParams
  76. final def getMaxLeaves: Int
    Definition Classes
    BoosterParams
  77. final def getMaximizeEvaluationMetrics: Boolean
    Definition Classes
    LearningTaskParams
  78. final def getMinChildWeight: Double
    Definition Classes
    BoosterParams
  79. final def getMissing: Float
    Definition Classes
    GeneralParams
  80. final def getMonotoneConstraints: String
    Definition Classes
    BoosterParams
  81. final def getNormalizeType: String
    Definition Classes
    BoosterParams
  82. final def getNthread: Int
    Definition Classes
    GeneralParams
  83. final def getNumClass: Int

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

    growth policy for fast histogram algorithm

    growth policy for fast histogram algorithm

    Definition Classes
    BoosterParams
  111. 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
  112. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  113. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  114. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  115. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  116. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  117. final val interactionConstraints: Param[String]
    Definition Classes
    BoosterParams
  118. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  119. 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
  120. def isFeaturesColsValid: Boolean

    Check if featuresCols is valid

    Check if featuresCols is valid

    Definition Classes
    HasFeaturesCols
  121. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  122. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  123. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  124. final val labelCol: Param[String]
    Definition Classes
    HasLabelCol
  125. 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
  126. 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
  127. final val leafPredictionCol: Param[String]

    Param for leaf prediction column name.

    Param for leaf prediction column name.

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

    maximum number of bins in histogram

    maximum number of bins in histogram

    Definition Classes
    BoosterParams
  141. 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
  142. 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
  143. 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
  144. final val maximizeEvaluationMetrics: BooleanParam
    Definition Classes
    LearningTaskParams
  145. 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
  146. final val missing: FloatParam

    the value treated as missing.

    the value treated as missing. default: Float.NaN

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

    number of threads used by per worker.

    number of threads used by per worker. default 1

    Definition Classes
    GeneralParams
  154. final val numClass: IntParam

    number of classes

    number of classes

    Definition Classes
    HasNumClass
  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. final val numRound: IntParam

    The number of rounds for boosting

    The number of rounds for boosting

    Definition Classes
    GeneralParams
  157. 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
  158. final val objective: Param[String]

    Specify the learning task and the corresponding learning objective.

    Specify the learning task and the corresponding learning objective. options: reg:squarederror, reg:squaredlogerror, reg:logistic, binary:logistic, binary:logitraw, count:poisson, multi:softmax, multi:softprob, rank:ndcg, reg:gamma. default: reg:squarederror

    Definition Classes
    LearningTaskParams
  159. 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
  160. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  161. final val predictionCol: Param[String]
    Definition Classes
    HasPredictionCol
  162. final val probabilityCol: Param[String]
    Definition Classes
    HasProbabilityCol
  163. final def rabitConnectRetry: IntParam
    Definition Classes
    RabitParams
  164. 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
  165. final def rabitTimeout: IntParam
    Definition Classes
    RabitParams
  166. 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
  167. final val rawPredictionCol: Param[String]
    Definition Classes
    HasRawPredictionCol
  168. 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
  169. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  170. 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
  171. 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
  172. final def set(paramPair: ParamPair[_]): XGBoostClassifier.this.type
    Attributes
    protected
    Definition Classes
    Params
  173. final def set(param: String, value: Any): XGBoostClassifier.this.type
    Attributes
    protected
    Definition Classes
    Params
  174. final def set[T](param: Param[T], value: T): XGBoostClassifier.this.type
    Definition Classes
    Params
  175. def setAllowNonZeroForMissing(value: Boolean): XGBoostClassifier.this.type
  176. def setAlpha(value: Double): XGBoostClassifier.this.type
  177. def setBaseMarginCol(value: String): XGBoostClassifier.this.type
  178. def setBaseScore(value: Double): XGBoostClassifier.this.type
  179. def setCheckpointInterval(value: Int): XGBoostClassifier.this.type
  180. def setCheckpointPath(value: String): XGBoostClassifier.this.type
  181. def setColsampleBylevel(value: Double): XGBoostClassifier.this.type
  182. def setColsampleBytree(value: Double): XGBoostClassifier.this.type
  183. def setCustomEval(value: EvalTrait): XGBoostClassifier.this.type
  184. def setCustomObj(value: ObjectiveTrait): XGBoostClassifier.this.type
  185. final def setDefault(paramPairs: ParamPair[_]*): XGBoostClassifier.this.type
    Attributes
    protected
    Definition Classes
    Params
  186. final def setDefault[T](param: Param[T], value: T): XGBoostClassifier.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  187. def setDevice(value: String): XGBoostClassifier.this.type
  188. def setEta(value: Double): XGBoostClassifier.this.type
  189. def setEvalMetric(value: String): XGBoostClassifier.this.type
  190. def setEvalSets(evalSets: Map[String, DataFrame]): XGBoostClassifier.this.type
    Definition Classes
    NonParamVariables
  191. def setFeatureNames(value: Array[String]): XGBoostClassifier.this.type
  192. def setFeatureTypes(value: Array[String]): XGBoostClassifier.this.type
  193. def setFeaturesCol(value: Array[String]): XGBoostClassifier.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
  194. def setFeaturesCol(value: String): XGBoostClassifier
    Definition Classes
    Predictor
  195. def setGamma(value: Double): XGBoostClassifier.this.type
  196. def setGrowPolicy(value: String): XGBoostClassifier.this.type
  197. def setHandleInvalid(value: String): XGBoostClassifier.this.type

    Set the handleInvalid for VectorAssembler

    Set the handleInvalid for VectorAssembler

    Definition Classes
    XGBoostEstimatorCommon
  198. def setLabelCol(value: String): XGBoostClassifier
    Definition Classes
    Predictor
  199. def setLambda(value: Double): XGBoostClassifier.this.type
  200. def setLambdaBias(value: Double): XGBoostClassifier.this.type
  201. def setMaxBins(value: Int): XGBoostClassifier.this.type
  202. def setMaxDeltaStep(value: Double): XGBoostClassifier.this.type
  203. def setMaxDepth(value: Int): XGBoostClassifier.this.type
  204. def setMaxLeaves(value: Int): XGBoostClassifier.this.type
  205. def setMaximizeEvaluationMetrics(value: Boolean): XGBoostClassifier.this.type
  206. def setMinChildWeight(value: Double): XGBoostClassifier.this.type
  207. def setMissing(value: Float): XGBoostClassifier.this.type
  208. def setNormalizeType(value: String): XGBoostClassifier.this.type
  209. def setNthread(value: Int): XGBoostClassifier.this.type
  210. def setNumClass(value: Int): XGBoostClassifier.this.type
  211. def setNumEarlyStoppingRounds(value: Int): XGBoostClassifier.this.type
  212. def setNumRound(value: Int): XGBoostClassifier.this.type
  213. def setNumWorkers(value: Int): XGBoostClassifier.this.type
  214. def setObjective(value: String): XGBoostClassifier.this.type
  215. def setObjectiveType(value: String): XGBoostClassifier.this.type
  216. def setPredictionCol(value: String): XGBoostClassifier
    Definition Classes
    Predictor
  217. def setProbabilityCol(value: String): XGBoostClassifier
    Definition Classes
    ProbabilisticClassifier
  218. def setRateDrop(value: Double): XGBoostClassifier.this.type
  219. def setRawPredictionCol(value: String): XGBoostClassifier
    Definition Classes
    Classifier
  220. def setSampleType(value: String): XGBoostClassifier.this.type
  221. def setScalePosWeight(value: Double): XGBoostClassifier.this.type
  222. def setSeed(value: Long): XGBoostClassifier.this.type
  223. def setSilent(value: Int): XGBoostClassifier.this.type
  224. def setSinglePrecisionHistogram(value: Boolean): XGBoostClassifier.this.type
  225. def setSkipDrop(value: Double): XGBoostClassifier.this.type
  226. def setSubsample(value: Double): XGBoostClassifier.this.type
  227. def setThresholds(value: Array[Double]): XGBoostClassifier
    Definition Classes
    ProbabilisticClassifier
  228. def setTrainTestRatio(value: Double): XGBoostClassifier.this.type
  229. def setTreeMethod(value: String): XGBoostClassifier.this.type
  230. def setUseExternalMemory(value: Boolean): XGBoostClassifier.this.type
  231. def setWeightCol(value: String): XGBoostClassifier.this.type
  232. final val silent: IntParam

    Deprecated.

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

    Definition Classes
    GeneralParams
  233. 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
  234. 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
  235. 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
  236. 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
  237. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  238. val thresholds: DoubleArrayParam
    Definition Classes
    HasThresholds
  239. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  240. 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
  241. def train(dataset: Dataset[_]): XGBoostClassificationModel
    Attributes
    protected
    Definition Classes
    XGBoostClassifier → Predictor
  242. final val trainTestRatio: DoubleParam

    Fraction of training points to use for testing.

    Fraction of training points to use for testing.

    Definition Classes
    LearningTaskParams
    Annotations
    @Deprecated
  243. def transformSchema(schema: StructType): StructType
    Definition Classes
    XGBoostClassifier → Predictor → PipelineStage
  244. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  245. def transformSchemaWithFeaturesCols(fit: Boolean, schema: StructType): StructType

    check the features columns type

    check the features columns type

    Definition Classes
    XGBoostEstimatorCommon
  246. final val treeLimit: IntParam
    Definition Classes
    BoosterParams
  247. 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
  248. val uid: String
    Definition Classes
    XGBoostClassifier → Identifiable
  249. final val useExternalMemory: BooleanParam

    whether to use external memory as cache.

    whether to use external memory as cache. default: false

    Definition Classes
    GeneralParams
  250. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
    Attributes
    protected
    Definition Classes
    ProbabilisticClassifierParams → ClassifierParams → PredictorParams
  251. 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
  252. 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
  253. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  254. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  255. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  256. final val weightCol: Param[String]
    Definition Classes
    HasWeightCol
  257. def write: MLWriter
    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from DefaultParamsWritable

Inherited from MLWritable

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 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 Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

getParam

param

Ungrouped