Packages

class TrainRegressor extends Estimator[TrainedRegressorModel] with AutoTrainer[TrainedRegressorModel] with BasicLogging

Trains a regression model.

Linear Supertypes
BasicLogging, AutoTrainer[TrainedRegressorModel], Wrappable, RWrappable, PythonWrappable, BaseWrappable, HasFeaturesCol, ComplexParamsWritable, MLWritable, HasLabelCol, Estimator[TrainedRegressorModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By Inheritance
Inherited
  1. TrainRegressor
  2. BasicLogging
  3. AutoTrainer
  4. Wrappable
  5. RWrappable
  6. PythonWrappable
  7. BaseWrappable
  8. HasFeaturesCol
  9. ComplexParamsWritable
  10. MLWritable
  11. HasLabelCol
  12. Estimator
  13. PipelineStage
  14. Logging
  15. Params
  16. Serializable
  17. Serializable
  18. Identifiable
  19. AnyRef
  20. Any
  1. Hide All
  2. Show All
Visibility
  1. Public
  2. All

Instance Constructors

  1. new TrainRegressor()
  2. new TrainRegressor(uid: String)

Value Members

  1. final def clear(param: Param[_]): TrainRegressor.this.type
    Definition Classes
    Params
  2. def copy(extra: ParamMap): Estimator[TrainedRegressorModel]
    Definition Classes
    TrainRegressor → Estimator → PipelineStage → Params
  3. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  4. def explainParams(): String
    Definition Classes
    Params
  5. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  6. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  7. val featuresCol: Param[String]

    The name of the features column

    The name of the features column

    Definition Classes
    HasFeaturesCol
  8. def fit(dataset: Dataset[_]): TrainedRegressorModel

    Fits the regression model.

    Fits the regression model.

    dataset

    The input dataset to train.

    returns

    The trained regression model.

    Definition Classes
    TrainRegressor → Estimator
  9. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[TrainedRegressorModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  10. def fit(dataset: Dataset[_], paramMap: ParamMap): TrainedRegressorModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  11. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): TrainedRegressorModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  12. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  13. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  14. def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  15. def getLabelCol: String

    Definition Classes
    HasLabelCol
  16. def getModel: Estimator[_ <: Model[_]]

    Definition Classes
    AutoTrainer
  17. def getNumFeatures: Int

    Definition Classes
    AutoTrainer
  18. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  19. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  20. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  21. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  22. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  23. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  24. val labelCol: Param[String]

    The name of the label column

    The name of the label column

    Definition Classes
    HasLabelCol
  25. def logClass(): Unit
    Definition Classes
    BasicLogging
  26. def logFit[T](f: ⇒ T): T
    Definition Classes
    BasicLogging
  27. def logPredict[T](f: ⇒ T): T
    Definition Classes
    BasicLogging
  28. def logTrain[T](f: ⇒ T): T
    Definition Classes
    BasicLogging
  29. def logTransform[T](f: ⇒ T): T
    Definition Classes
    BasicLogging
  30. def logVerb[T](verb: String, f: ⇒ T): T
    Definition Classes
    BasicLogging
  31. def makePyFile(conf: CodegenConfig): Unit
    Definition Classes
    PythonWrappable
  32. def makeRFile(conf: CodegenConfig): Unit
    Definition Classes
    RWrappable
  33. val model: EstimatorParam

    Model to run.

    Model to run. See doc on derived classes.

    Definition Classes
    AutoTrainer
  34. def modelDoc: String

    Doc for model to run.

    Doc for model to run.

    Definition Classes
    TrainRegressor → AutoTrainer
  35. val numFeatures: IntParam

    Number of features to hash to

    Number of features to hash to

    Definition Classes
    AutoTrainer
  36. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  37. def pyAdditionalMethods: String
    Definition Classes
    PythonWrappable
  38. def pyInitFunc(): String
    Definition Classes
    PythonWrappable
  39. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  40. final def set[T](param: Param[T], value: T): TrainRegressor.this.type
    Definition Classes
    Params
  41. def setFeaturesCol(value: String): TrainRegressor.this.type

    Definition Classes
    HasFeaturesCol
  42. def setLabelCol(value: String): TrainRegressor.this.type

    Definition Classes
    HasLabelCol
  43. def setModel(value: Estimator[_ <: Model[_]]): TrainRegressor.this.type

    Definition Classes
    AutoTrainer
  44. def setNumFeatures(value: Int): TrainRegressor.this.type

    Definition Classes
    AutoTrainer
  45. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  46. def transformSchema(schema: StructType): StructType
    Definition Classes
    TrainRegressor → PipelineStage
    Annotations
    @DeveloperApi()
  47. val uid: String
    Definition Classes
    TrainRegressor → BasicLogging → Identifiable
  48. val ver: String
    Definition Classes
    BasicLogging
  49. def write: MLWriter
    Definition Classes
    ComplexParamsWritable → MLWritable