Class/Object

com.microsoft.ml.spark.train

TrainRegressor

Related Docs: object TrainRegressor | package train

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

Trains a regression model.

Annotations
@InternalWrapper()
Linear Supertypes
AutoTrainer[TrainedRegressorModel], HasFeaturesCol, ComplexParamsWritable, MLWritable, HasLabelCol, Wrappable, Estimator[TrainedRegressorModel], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. TrainRegressor
  2. AutoTrainer
  3. HasFeaturesCol
  4. ComplexParamsWritable
  5. MLWritable
  6. HasLabelCol
  7. Wrappable
  8. Estimator
  9. PipelineStage
  10. Logging
  11. Params
  12. Serializable
  13. Serializable
  14. Identifiable
  15. AnyRef
  16. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new TrainRegressor()

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  2. new TrainRegressor(uid: String)

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Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any

  3. final def $[T](param: Param[T]): T

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    Attributes
    protected
    Definition Classes
    Params

  4. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  5. def additionalPythonMethods(): String

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    Definition Classes
    Wrappable

  6. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  7. final def clear(param: Param[_]): TrainRegressor.this.type

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    Definition Classes
    Params

  8. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  9. def copy(extra: ParamMap): Estimator[TrainedRegressorModel]

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    Definition Classes
    TrainRegressor → Estimator → PipelineStage → Params

  10. def copyValues[T <: Params](to: T, extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params

  11. final def defaultCopy[T <: Params](extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params

  12. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef

  13. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  14. def explainParam(param: Param[_]): String

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    Definition Classes
    Params

  15. def explainParams(): String

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    Definition Classes
    Params

  16. final def extractParamMap(): ParamMap

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    Definition Classes
    Params

  17. final def extractParamMap(extra: ParamMap): ParamMap

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    Definition Classes
    Params

  18. val featuresCol: Param[String]

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    The name of the features column

    The name of the features column

    Definition Classes
    HasFeaturesCol

  19. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  20. def fit(dataset: Dataset[_]): TrainedRegressorModel

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    Fits the regression model.

    Fits the regression model.

    dataset

    The input dataset to train.

    returns

    The trained regression model.

    Definition Classes
    TrainRegressor → Estimator

  21. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[TrainedRegressorModel]

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )

  22. def fit(dataset: Dataset[_], paramMap: ParamMap): TrainedRegressorModel

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )

  23. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): TrainedRegressorModel

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()

  24. final def get[T](param: Param[T]): Option[T]

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    Definition Classes
    Params

  25. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any

  26. final def getDefault[T](param: Param[T]): Option[T]

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    Definition Classes
    Params

  27. def getFeaturesCol: String

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    Definition Classes
    HasFeaturesCol

  28. def getLabelCol: String

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    Definition Classes
    HasLabelCol

  29. def getModel: Estimator[_ <: Model[_]]

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    Definition Classes
    AutoTrainer

  30. def getNumFeatures: Int

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    Definition Classes
    AutoTrainer

  31. final def getOrDefault[T](param: Param[T]): T

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    Definition Classes
    Params

  32. def getParam(paramName: String): Param[Any]

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    Definition Classes
    Params

  33. final def hasDefault[T](param: Param[T]): Boolean

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    Definition Classes
    Params

  34. def hasParam(paramName: String): Boolean

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    Definition Classes
    Params

  35. def hashCode(): Int

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    Definition Classes
    AnyRef → Any

  36. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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    Attributes
    protected
    Definition Classes
    Logging

  37. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  38. final def isDefined(param: Param[_]): Boolean

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    Definition Classes
    Params

  39. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  40. final def isSet(param: Param[_]): Boolean

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    Definition Classes
    Params

  41. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging

  42. val labelCol: Param[String]

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    The name of the label column

    The name of the label column

    Definition Classes
    HasLabelCol

  43. def log: Logger

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    Attributes
    protected
    Definition Classes
    Logging

  44. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  45. def logDebug(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  46. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  47. def logError(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  48. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  49. def logInfo(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  50. def logName: String

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    Attributes
    protected
    Definition Classes
    Logging

  51. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  52. def logTrace(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  53. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  54. def logWarning(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  55. val model: EstimatorParam

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    Model to run.

    Model to run. See doc on derived classes.

    Definition Classes
    AutoTrainer

  56. def modelDoc: String

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    Doc for model to run.

    Doc for model to run.

    Definition Classes
    TrainRegressor → AutoTrainer

  57. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef

  58. final def notify(): Unit

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    Definition Classes
    AnyRef

  59. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  60. val numFeatures: IntParam

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    Number of features to hash to

    Number of features to hash to

    Definition Classes
    AutoTrainer

  61. lazy val params: Array[Param[_]]

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    Definition Classes
    Params

  62. def save(path: String): Unit

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    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )

  63. final def set(paramPair: ParamPair[_]): TrainRegressor.this.type

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    Attributes
    protected
    Definition Classes
    Params

  64. final def set(param: String, value: Any): TrainRegressor.this.type

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    Attributes
    protected
    Definition Classes
    Params

  65. final def set[T](param: Param[T], value: T): TrainRegressor.this.type

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    Definition Classes
    Params

  66. final def setDefault(paramPairs: ParamPair[_]*): TrainRegressor.this.type

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    Attributes
    protected
    Definition Classes
    Params

  67. final def setDefault[T](param: Param[T], value: T): TrainRegressor.this.type

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    Attributes
    protected
    Definition Classes
    Params

  68. def setFeaturesCol(value: String): TrainRegressor.this.type

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    Definition Classes
    HasFeaturesCol

  69. def setLabelCol(value: String): TrainRegressor.this.type

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    Definition Classes
    HasLabelCol

  70. def setModel(value: Estimator[_ <: Model[_]]): TrainRegressor.this.type

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    Definition Classes
    AutoTrainer

  71. def setNumFeatures(value: Int): TrainRegressor.this.type

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    Definition Classes
    AutoTrainer

  72. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef

  73. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any

  74. def transformSchema(schema: StructType): StructType

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    Definition Classes
    TrainRegressor → PipelineStage
    Annotations
    @DeveloperApi()

  75. def transformSchema(schema: StructType, logging: Boolean): StructType

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    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()

  76. val uid: String

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    Definition Classes
    TrainRegressor → Identifiable

  77. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  78. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  79. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  80. def write: MLWriter

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    Definition Classes
    ComplexParamsWritable → MLWritable

Inherited from AutoTrainer[TrainedRegressorModel]

Inherited from HasFeaturesCol

Inherited from ComplexParamsWritable

Inherited from MLWritable

Inherited from HasLabelCol

Inherited from Wrappable

Inherited from Estimator[TrainedRegressorModel]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

getParam

param

setParam

Ungrouped