Packages

class TrainedRegressorModel extends AutoTrainedModel[TrainedRegressorModel] with Wrappable with BasicLogging

Model produced by TrainRegressor.

Linear Supertypes
BasicLogging, Wrappable, RWrappable, PythonWrappable, BaseWrappable, AutoTrainedModel[TrainedRegressorModel], HasFeaturesCol, HasLabelCol, ComplexParamsWritable, MLWritable, Model[TrainedRegressorModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. TrainedRegressorModel
  2. BasicLogging
  3. Wrappable
  4. RWrappable
  5. PythonWrappable
  6. BaseWrappable
  7. AutoTrainedModel
  8. HasFeaturesCol
  9. HasLabelCol
  10. ComplexParamsWritable
  11. MLWritable
  12. Model
  13. Transformer
  14. PipelineStage
  15. Logging
  16. Params
  17. Serializable
  18. Serializable
  19. Identifiable
  20. AnyRef
  21. Any
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Visibility
  1. Public
  2. All

Instance Constructors

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

    uid

    The id of the module

Value Members

  1. final def clear(param: Param[_]): TrainedRegressorModel.this.type
    Definition Classes
    Params
  2. def copy(extra: ParamMap): TrainedRegressorModel
    Definition Classes
    TrainedRegressorModel → Model → Transformer → 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. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  9. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  10. def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  11. def getLabelCol: String

    Definition Classes
    HasLabelCol
  12. def getLastStage: Transformer

    Retrieve the underlying model.

    Retrieve the underlying model.

    returns

    The underlying model.

    Definition Classes
    AutoTrainedModel
  13. def getModel: PipelineModel
    Definition Classes
    AutoTrainedModel
  14. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  15. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  16. def getParamMap: ParamMap

    Retrieve the param map from the underlying model.

    Retrieve the param map from the underlying model.

    returns

    The param map from the underlying model.

    Definition Classes
    AutoTrainedModel
  17. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  18. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  19. def hasParent: Boolean
    Definition Classes
    Model
  20. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  21. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  22. val labelCol: Param[String]

    The name of the label column

    The name of the label column

    Definition Classes
    HasLabelCol
  23. def logClass(): Unit
    Definition Classes
    BasicLogging
  24. def logFit[T](f: ⇒ T): T
    Definition Classes
    BasicLogging
  25. def logPredict[T](f: ⇒ T): T
    Definition Classes
    BasicLogging
  26. def logTrain[T](f: ⇒ T): T
    Definition Classes
    BasicLogging
  27. def logTransform[T](f: ⇒ T): T
    Definition Classes
    BasicLogging
  28. def logVerb[T](verb: String, f: ⇒ T): T
    Definition Classes
    BasicLogging
  29. def makePyFile(conf: CodegenConfig): Unit
    Definition Classes
    PythonWrappable
  30. def makeRFile(conf: CodegenConfig): Unit
    Definition Classes
    RWrappable
  31. val model: TransformerParam
    Definition Classes
    AutoTrainedModel
  32. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  33. var parent: Estimator[TrainedRegressorModel]
    Definition Classes
    Model
  34. def pyAdditionalMethods: String
    Definition Classes
    PythonWrappable
  35. def pyInitFunc(): String
    Definition Classes
    PythonWrappable
  36. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  37. final def set[T](param: Param[T], value: T): TrainedRegressorModel.this.type
    Definition Classes
    Params
  38. def setFeaturesCol(value: String): TrainedRegressorModel.this.type

    Definition Classes
    HasFeaturesCol
  39. def setLabelCol(value: String): TrainedRegressorModel.this.type

    Definition Classes
    HasLabelCol
  40. def setModel(v: PipelineModel): TrainedRegressorModel.this.type
    Definition Classes
    AutoTrainedModel
  41. def setParent(parent: Estimator[TrainedRegressorModel]): TrainedRegressorModel
    Definition Classes
    Model
  42. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  43. def transform(dataset: Dataset[_]): DataFrame
    Definition Classes
    TrainedRegressorModel → Transformer
  44. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  45. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  46. def transformSchema(schema: StructType): StructType
    Definition Classes
    TrainedRegressorModel → PipelineStage
    Annotations
    @DeveloperApi()
  47. val uid: String
    Definition Classes
    TrainedRegressorModel → BasicLogging → Identifiable
  48. val ver: String
    Definition Classes
    BasicLogging
  49. def write: MLWriter
    Definition Classes
    ComplexParamsWritable → MLWritable