Class

org.incal.spark_ml.models.setting

RegressionLearningSetting

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case class RegressionLearningSetting(featuresNormalizationType: Option[VectorScalerType.Value] = None, outputNormalizationType: Option[VectorScalerType.Value] = None, pcaDims: Option[Int] = None, trainingTestSplitRatio: Option[Double] = None, repetitions: Option[Int] = None, crossValidationFolds: Option[Int] = None, crossValidationEvalMetric: Option[regression.RegressionEvalMetric.Value] = None, collectOutputs: Boolean = false) extends LearningSetting[regression.RegressionEvalMetric.Value] with Product with Serializable

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Serializable, Serializable, Product, Equals, LearningSetting[regression.RegressionEvalMetric.Value], AnyRef, Any
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  1. RegressionLearningSetting
  2. Serializable
  3. Serializable
  4. Product
  5. Equals
  6. LearningSetting
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Instance Constructors

  1. new RegressionLearningSetting(featuresNormalizationType: Option[VectorScalerType.Value] = None, outputNormalizationType: Option[VectorScalerType.Value] = None, pcaDims: Option[Int] = None, trainingTestSplitRatio: Option[Double] = None, repetitions: Option[Int] = None, crossValidationFolds: Option[Int] = None, crossValidationEvalMetric: Option[regression.RegressionEvalMetric.Value] = None, collectOutputs: Boolean = false)

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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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    AnyRef → Any

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

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    AnyRef → Any

  4. final def asInstanceOf[T0]: T0

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  5. def clone(): AnyRef

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

  6. val collectOutputs: Boolean

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  7. val crossValidationEvalMetric: Option[regression.RegressionEvalMetric.Value]

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    Definition Classes
    RegressionLearningSetting → LearningSetting

  8. val crossValidationFolds: Option[Int]

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    Definition Classes
    RegressionLearningSetting → LearningSetting

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

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    Definition Classes
    AnyRef

  10. val featuresNormalizationType: Option[VectorScalerType.Value]

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    Definition Classes
    RegressionLearningSetting → LearningSetting

  11. def finalize(): Unit

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    protected[java.lang]
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    Annotations
    @throws( classOf[java.lang.Throwable] )

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

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    AnyRef → Any

  13. final def isInstanceOf[T0]: Boolean

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

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  15. final def notify(): Unit

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  16. final def notifyAll(): Unit

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  17. val outputNormalizationType: Option[VectorScalerType.Value]

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  18. val pcaDims: Option[Int]

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    Definition Classes
    RegressionLearningSetting → LearningSetting

  19. val repetitions: Option[Int]

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    Definition Classes
    RegressionLearningSetting → LearningSetting

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

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    Definition Classes
    AnyRef

  21. val trainingTestSplitRatio: Option[Double]

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    Definition Classes
    RegressionLearningSetting → LearningSetting

  22. final def wait(): Unit

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    @throws( ... )

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

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

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Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from LearningSetting[regression.RegressionEvalMetric.Value]

Inherited from AnyRef

Inherited from Any

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