Packages

class TrainedRegressorModel extends AutoTrainedModel[TrainedRegressorModel] with Wrappable with SynapseMLLogging

Model produced by TrainRegressor.

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Inherited
  1. TrainedRegressorModel
  2. SynapseMLLogging
  3. Wrappable
  4. DotnetWrappable
  5. RWrappable
  6. PythonWrappable
  7. BaseWrappable
  8. AutoTrainedModel
  9. HasFeaturesCol
  10. HasLabelCol
  11. ComplexParamsWritable
  12. MLWritable
  13. Model
  14. Transformer
  15. PipelineStage
  16. Logging
  17. Params
  18. Serializable
  19. Serializable
  20. Identifiable
  21. AnyRef
  22. 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 dotnetAdditionalMethods: String
    Definition Classes
    DotnetWrappable
  4. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  5. def explainParams(): String
    Definition Classes
    Params
  6. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  7. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  8. val featuresCol: Param[String]

    The name of the features column

    The name of the features column

    Definition Classes
    HasFeaturesCol
  9. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  10. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  11. def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  12. def getLabelCol: String

    Definition Classes
    HasLabelCol
  13. def getLastStage: Transformer

    Retrieve the underlying model.

    Retrieve the underlying model.

    returns

    The underlying model.

    Definition Classes
    AutoTrainedModel
  14. def getModel: PipelineModel
    Definition Classes
    AutoTrainedModel
  15. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  16. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  17. def getParamInfo(p: Param[_]): ParamInfo[_]
    Definition Classes
    BaseWrappable
  18. 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
  19. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  20. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  21. def hasParent: Boolean
    Definition Classes
    Model
  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(featureName: String): Unit
    Definition Classes
    SynapseMLLogging
  26. def logFit[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  27. def logTransform[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  28. def logVerb[T](verb: String, f: ⇒ T, columns: Option[Int] = None): T
    Definition Classes
    SynapseMLLogging
  29. def makeDotnetFile(conf: CodegenConfig): Unit
    Definition Classes
    DotnetWrappable
  30. def makePyFile(conf: CodegenConfig): Unit
    Definition Classes
    PythonWrappable
  31. def makeRFile(conf: CodegenConfig): Unit
    Definition Classes
    RWrappable
  32. val model: TransformerParam
    Definition Classes
    AutoTrainedModel
  33. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  34. var parent: Estimator[TrainedRegressorModel]
    Definition Classes
    Model
  35. def pyAdditionalMethods: String
    Definition Classes
    PythonWrappable
  36. def pyInitFunc(): String
    Definition Classes
    PythonWrappable
  37. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  38. final def set[T](param: Param[T], value: T): TrainedRegressorModel.this.type
    Definition Classes
    Params
  39. def setFeaturesCol(value: String): TrainedRegressorModel.this.type

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

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