Packages

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

Trains a regression model.

Linear Supertypes
SynapseMLLogging, AutoTrainer[TrainedRegressorModel], Wrappable, DotnetWrappable, RWrappable, PythonWrappable, BaseWrappable, HasFeaturesCol, ComplexParamsWritable, MLWritable, HasInputCols, HasLabelCol, Estimator[TrainedRegressorModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. TrainRegressor
  2. SynapseMLLogging
  3. AutoTrainer
  4. Wrappable
  5. DotnetWrappable
  6. RWrappable
  7. PythonWrappable
  8. BaseWrappable
  9. HasFeaturesCol
  10. ComplexParamsWritable
  11. MLWritable
  12. HasInputCols
  13. HasLabelCol
  14. Estimator
  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 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 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. 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
  10. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[TrainedRegressorModel]
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  11. def fit(dataset: Dataset[_], paramMap: ParamMap): TrainedRegressorModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  12. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): TrainedRegressorModel
    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  13. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  14. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  15. def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  16. def getInputCols: Array[String]

    Definition Classes
    HasInputCols
  17. def getLabelCol: String

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

    Definition Classes
    AutoTrainer
  19. def getNumFeatures: Int

    Definition Classes
    AutoTrainer
  20. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  21. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  22. def getParamInfo(p: Param[_]): ParamInfo[_]
    Definition Classes
    BaseWrappable
  23. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  24. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  25. val inputCols: StringArrayParam

    The names of the inputColumns

    The names of the inputColumns

    Definition Classes
    HasInputCols
  26. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  27. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  28. val labelCol: Param[String]

    The name of the label column

    The name of the label column

    Definition Classes
    HasLabelCol
  29. def logClass(featureName: String): Unit
    Definition Classes
    SynapseMLLogging
  30. def logFit[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  31. def logTransform[T](f: ⇒ T, columns: Int): T
    Definition Classes
    SynapseMLLogging
  32. def logVerb[T](verb: String, f: ⇒ T, columns: Option[Int] = None): T
    Definition Classes
    SynapseMLLogging
  33. def makeDotnetFile(conf: CodegenConfig): Unit
    Definition Classes
    DotnetWrappable
  34. def makePyFile(conf: CodegenConfig): Unit
    Definition Classes
    PythonWrappable
  35. def makeRFile(conf: CodegenConfig): Unit
    Definition Classes
    RWrappable
  36. val model: EstimatorParam

    Model to run.

    Model to run. See doc on derived classes.

    Definition Classes
    AutoTrainer
  37. def modelDoc: String

    Doc for model to run.

    Doc for model to run.

    Definition Classes
    TrainRegressor → AutoTrainer
  38. val numFeatures: IntParam

    Number of features to hash to

    Number of features to hash to

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

    Definition Classes
    HasFeaturesCol
  45. def setInputCols(value: Array[String]): TrainRegressor.this.type

    Definition Classes
    HasInputCols
  46. def setLabelCol(value: String): TrainRegressor.this.type

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

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

    Definition Classes
    AutoTrainer
  49. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  50. def transformSchema(schema: StructType): StructType
    Definition Classes
    TrainRegressor → PipelineStage
    Annotations
    @DeveloperApi()
  51. val uid: String
    Definition Classes
    TrainRegressor → SynapseMLLogging → Identifiable
  52. def write: MLWriter
    Definition Classes
    ComplexParamsWritable → MLWritable