Packages

class TrainedRegressorModel extends AutoTrainedModel[TrainedRegressorModel] with Wrappable with SynapseMLLogging

Model produced by TrainRegressor.

Linear Supertypes
SynapseMLLogging, 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. SynapseMLLogging
  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 getParamInfo(p: Param[_]): ParamInfo[_]
    Definition Classes
    BaseWrappable
  17. 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
  18. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  19. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  20. def hasParent: Boolean
    Definition Classes
    Model
  21. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  22. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  23. val labelCol: Param[String]

    The name of the label column

    The name of the label column

    Definition Classes
    HasLabelCol
  24. def logClass(featureName: String): Unit
    Definition Classes
    SynapseMLLogging
  25. def logFit[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  26. def logTransform[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  27. def logVerb[T](verb: String, f: ⇒ T, columns: Option[Int] = None): T
    Definition Classes
    SynapseMLLogging
  28. def makePyFile(conf: CodegenConfig): Unit
    Definition Classes
    PythonWrappable
  29. def makeRFile(conf: CodegenConfig): Unit
    Definition Classes
    RWrappable
  30. val model: TransformerParam
    Definition Classes
    AutoTrainedModel
  31. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  32. var parent: Estimator[TrainedRegressorModel]
    Definition Classes
    Model
  33. def pyAdditionalMethods: String
    Definition Classes
    PythonWrappable
  34. def pyInitFunc(): String
    Definition Classes
    PythonWrappable
  35. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  36. final def set[T](param: Param[T], value: T): TrainedRegressorModel.this.type
    Definition Classes
    Params
  37. def setFeaturesCol(value: String): TrainedRegressorModel.this.type

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

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