Packages

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

Trains a regression model.

Linear Supertypes
SynapseMLLogging, AutoTrainer[TrainedRegressorModel], Wrappable, 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. RWrappable
  6. PythonWrappable
  7. BaseWrappable
  8. HasFeaturesCol
  9. ComplexParamsWritable
  10. MLWritable
  11. HasInputCols
  12. HasLabelCol
  13. Estimator
  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 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 getInputCols: Array[String]

    Definition Classes
    HasInputCols
  16. def getLabelCol: String

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

    Definition Classes
    AutoTrainer
  18. def getNumFeatures: Int

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

    The names of the inputColumns

    The names of the inputColumns

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

    The name of the label column

    The name of the label column

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

    Model to run.

    Model to run. See doc on derived classes.

    Definition Classes
    AutoTrainer
  35. def modelDoc: String

    Doc for model to run.

    Doc for model to run.

    Definition Classes
    TrainRegressor → AutoTrainer
  36. val numFeatures: IntParam

    Number of features to hash to

    Number of features to hash to

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

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

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

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

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

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