Packages

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

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

Instance Constructors

  1. new XGBoostClassifier(xgboostParams: Map[String, Any])
  2. new XGBoostClassifier(uid: String)
  3. new XGBoostClassifier()
  4. new XGBoostClassifier(uid: String, xgboostParams: Map[String, Any])

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T
    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. def MLlib2XGBoostParams: Map[String, Any]
    Definition Classes
    ParamMapFuncs
  6. def XGBoost2MLlibParams(xgboostParams: Map[String, Any]): Unit
    Definition Classes
    ParamMapFuncs
  7. final val allowNonZeroForMissing: BooleanParam

    Allows for having a non-zero value for missing when training on prediction on a Sparse or Empty vector.

    Allows for having a non-zero value for missing when training on prediction on a Sparse or Empty vector.

    Definition Classes
    GeneralParams
  8. final val alpha: DoubleParam

    L1 regularization term on weights, increase this value will make model more conservative.

    L1 regularization term on weights, increase this value will make model more conservative. [default=0]

    Definition Classes
    BoosterParams
  9. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  10. final val baseMarginCol: Param[String]

    Param for initial prediction (aka base margin) column name.

    Param for initial prediction (aka base margin) column name.

    Definition Classes
    HasBaseMarginCol
  11. final val baseScore: DoubleParam

    the initial prediction score of all instances, global bias.

    the initial prediction score of all instances, global bias. default=0.5

    Definition Classes
    LearningTaskParams
  12. final val cacheTrainingSet: BooleanParam

    whether caching training data

    whether caching training data

    Definition Classes
    LearningTaskParams
  13. final val checkpointInterval: IntParam

    Param for set checkpoint interval (>= 1) or disable checkpoint (-1).

    Param for set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the trained model will get checkpointed every 10 iterations. Note: checkpoint_path must also be set if the checkpoint interval is greater than 0.

    Definition Classes
    GeneralParams
  14. final val checkpointPath: Param[String]

    The hdfs folder to load and save checkpoint boosters.

    The hdfs folder to load and save checkpoint boosters. default: empty_string

    Definition Classes
    GeneralParams
  15. final def clear(param: Param[_]): XGBoostClassifier.this.type
    Definition Classes
    Params
  16. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  17. final val colsampleBylevel: DoubleParam

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

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

    Definition Classes
    BoosterParams
  18. final val colsampleBytree: DoubleParam

    subsample ratio of columns when constructing each tree.

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

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

    Param for contribution prediction column name.

    Param for contribution prediction column name.

    Definition Classes
    HasContribPredictionCol
  20. def copy(extra: ParamMap): XGBoostClassifier
    Definition Classes
    XGBoostClassifier → Predictor → Estimator → PipelineStage → Params
  21. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  22. final val customEval: CustomEvalParam

    customized evaluation function provided by user.

    customized evaluation function provided by user. default: null

    Definition Classes
    GeneralParams
  23. final val customObj: CustomObjParam

    customized objective function provided by user.

    customized objective function provided by user. default: null

    Definition Classes
    GeneralParams
  24. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  25. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  26. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  27. final val eta: DoubleParam

    step size shrinkage used in update to prevents overfitting.

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

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

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

    evaluation metrics for validation data, a default metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking). options: rmse, rmsle, mae, 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 extractLabeledPoints(dataset: Dataset[_], numClasses: Int): RDD[org.apache.spark.ml.feature.LabeledPoint]
    Attributes
    protected
    Definition Classes
    Classifier
  33. def extractLabeledPoints(dataset: Dataset[_]): RDD[org.apache.spark.ml.feature.LabeledPoint]
    Attributes
    protected
    Definition Classes
    Predictor
  34. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  35. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  36. final val featuresCol: Param[String]
    Definition Classes
    HasFeaturesCol
  37. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  38. def fit(dataset: Dataset[_]): XGBoostClassificationModel
    Definition Classes
    Predictor → Estimator
  39. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[XGBoostClassificationModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  40. def fit(dataset: Dataset[_], paramMap: ParamMap): XGBoostClassificationModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  41. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): XGBoostClassificationModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  42. 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
  43. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  44. final def getAllowNonZeroForMissingValue: Boolean
    Definition Classes
    GeneralParams
  45. final def getAlpha: Double
    Definition Classes
    BoosterParams
  46. final def getBaseMarginCol: String

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

    Definition Classes
    HasContribPredictionCol
  54. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  55. final def getEta: Double
    Definition Classes
    BoosterParams
  56. final def getEvalMetric: String
    Definition Classes
    LearningTaskParams
  57. def getEvalSets(params: Map[String, Any]): Map[String, DataFrame]
    Definition Classes
    NonParamVariables
  58. final def getFeaturesCol: String
    Definition Classes
    HasFeaturesCol
  59. final def getGamma: Double
    Definition Classes
    BoosterParams
  60. final def getGrowPolicy: String
    Definition Classes
    BoosterParams
  61. final def getInteractionConstraints: String
    Definition Classes
    BoosterParams
  62. final def getLabelCol: String
    Definition Classes
    HasLabelCol
  63. final def getLambda: Double
    Definition Classes
    BoosterParams
  64. final def getLambdaBias: Double
    Definition Classes
    BoosterParams
  65. final def getLeafPredictionCol: String

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

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

    growth policy for fast histogram algorithm

    growth policy for fast histogram algorithm

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

    Param for leaf prediction column name.

    Param for leaf prediction column name.

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

    maximum number of bins in histogram

    maximum number of bins in histogram

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

    the value treated as missing.

    the value treated as missing. default: Float.NaN

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

    number of threads used by per worker.

    number of threads used by per worker. default 1

    Definition Classes
    GeneralParams
  145. final val numClass: IntParam

    number of classes

    number of classes

    Definition Classes
    HasNumClass
  146. 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
  147. final val numRound: IntParam

    The number of rounds for boosting

    The number of rounds for boosting

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

    Deprecated.

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

    Definition Classes
    GeneralParams
  219. 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
  220. 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
  221. 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
  222. 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
  223. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  224. final val thresholds: DoubleArrayParam
    Definition Classes
    HasThresholds
  225. 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
  226. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  227. 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
  228. def train(dataset: Dataset[_]): XGBoostClassificationModel
    Attributes
    protected
    Definition Classes
    XGBoostClassifier → Predictor
  229. final val trainTestRatio: DoubleParam

    Fraction of training points to use for testing.

    Fraction of training points to use for testing.

    Definition Classes
    LearningTaskParams
  230. def transformSchema(schema: StructType): StructType
    Definition Classes
    Predictor → PipelineStage
  231. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  232. final val treeLimit: IntParam
    Definition Classes
    BoosterParams
  233. final val treeMethod: Param[String]

    The tree construction algorithm used in XGBoost.

    The tree construction algorithm used in XGBoost. options: {'auto', 'exact', 'approx'} [default='auto']

    Definition Classes
    BoosterParams
  234. val uid: String
    Definition Classes
    XGBoostClassifier → Identifiable
  235. final val useExternalMemory: BooleanParam

    whether to use external memory as cache.

    whether to use external memory as cache. default: false

    Definition Classes
    GeneralParams
  236. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
    Attributes
    protected
    Definition Classes
    ProbabilisticClassifierParams → ClassifierParams → PredictorParams
  237. 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
  238. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  239. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  240. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  241. final val weightCol: Param[String]
    Definition Classes
    HasWeightCol
  242. def write: MLWriter
    Definition Classes
    DefaultParamsWritable → MLWritable

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from XGBoostClassifierParams

Inherited from XGBoostEstimatorCommon

Inherited from NonParamVariables

Inherited from ParamMapFuncs

Inherited from RabitParams

Inherited from BoosterParams

Inherited from LearningTaskParams

Inherited from GeneralParams

Inherited from HasContribPredictionCol

Inherited from HasLeafPredictionCol

Inherited from HasNumClass

Inherited from HasBaseMarginCol

Inherited from HasWeightCol

Inherited from ProbabilisticClassifier[Vector, XGBoostClassifier, XGBoostClassificationModel]

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from Classifier[Vector, XGBoostClassifier, XGBoostClassificationModel]

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from Predictor[Vector, XGBoostClassifier, XGBoostClassificationModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Estimator[XGBoostClassificationModel]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

getParam

param

Ungrouped