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c

org.apache.spark.ml

BaseRegressor

abstract class BaseRegressor[F, R <: Regressor[F, R, M], M <: RegressionModel[F, M]] extends Regressor[F, R, M]

Temporary hack to expose private Regressor class in SparkML as a developer API

Linear Supertypes
Regressor[F, R, M], Predictor[F, R, M], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Estimator[M], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Known Subclasses
LightGBMRegressor, VowpalWabbitRegressor
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. BaseRegressor
  2. Regressor
  3. Predictor
  4. PredictorParams
  5. HasPredictionCol
  6. HasFeaturesCol
  7. HasLabelCol
  8. Estimator
  9. PipelineStage
  10. Logging
  11. Params
  12. Serializable
  13. Serializable
  14. Identifiable
  15. AnyRef
  16. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new BaseRegressor()

Abstract Value Members

  1. abstract def copy(extra: ParamMap): R
    Definition Classes
    Predictor → Estimator → PipelineStage → Params
  2. abstract val uid: String
    Definition Classes
    Identifiable

Concrete Value Members

  1. final def clear(param: Param[_]): BaseRegressor.this.type
    Definition Classes
    Params
  2. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  3. def explainParams(): String
    Definition Classes
    Params
  4. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  5. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  6. final val featuresCol: Param[String]
    Definition Classes
    HasFeaturesCol
  7. def fit(dataset: Dataset[_]): M
    Definition Classes
    Predictor → Estimator
  8. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[M]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  9. def fit(dataset: Dataset[_], paramMap: ParamMap): M
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  10. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): M
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  11. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  12. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  13. final def getFeaturesCol: String
    Definition Classes
    HasFeaturesCol
  14. final def getLabelCol: String
    Definition Classes
    HasLabelCol
  15. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  16. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  17. final def getPredictionCol: String
    Definition Classes
    HasPredictionCol
  18. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  19. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  20. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  21. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  22. final val labelCol: Param[String]
    Definition Classes
    HasLabelCol
  23. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  24. final val predictionCol: Param[String]
    Definition Classes
    HasPredictionCol
  25. final def set[T](param: Param[T], value: T): BaseRegressor.this.type
    Definition Classes
    Params
  26. def setFeaturesCol(value: String): R
    Definition Classes
    Predictor
  27. def setLabelCol(value: String): R
    Definition Classes
    Predictor
  28. def setPredictionCol(value: String): R
    Definition Classes
    Predictor
  29. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  30. def transformSchema(schema: StructType): StructType
    Definition Classes
    Predictor → PipelineStage