Packages

c

io.citrine.lolo.transformers

StandardizerPrediction

class StandardizerPrediction[T] extends PredictionResult[T]

Prediction that wraps the base prediction next to the transformation

Linear Supertypes
PredictionResult[T], AnyRef, Any
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  1. StandardizerPrediction
  2. PredictionResult
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Instance Constructors

  1. new StandardizerPrediction(baseResult: PredictionResult[T], trans: Seq[Option[(Double, Double)]])

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
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  2. final def ##(): Int
    Definition Classes
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  3. final def ==(arg0: Any): Boolean
    Definition Classes
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  4. final def asInstanceOf[T0]: T0
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  5. def clone(): AnyRef
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    protected[java.lang]
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    @native() @throws( ... )
  6. final def eq(arg0: AnyRef): Boolean
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  7. def equals(arg0: Any): Boolean
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  8. def finalize(): Unit
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    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  9. final def getClass(): Class[_]
    Definition Classes
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    @native()
  10. def getExpected(): Seq[T]

    Get the expected values for this prediction

    Get the expected values for this prediction

    Just reverse any transformation that was applied to the labels

    returns

    expected value of each prediction

    Definition Classes
    StandardizerPredictionPredictionResult
  11. def getGradient(): Option[Seq[Vector[Double]]]

    Get the gradient or sensitivity of each prediction

    Get the gradient or sensitivity of each prediction

    This is un-stanardized by rescaling by the label's variance divided by the feature transformation's variance

    returns

    a vector of doubles for each prediction

    Definition Classes
    StandardizerPredictionPredictionResult
  12. def getImportanceScores(): Option[Seq[Seq[Double]]]

    Get the training row scores for each prediction

    Get the training row scores for each prediction

    returns

    training row scores of each prediction

    Definition Classes
    PredictionResult
  13. def getInfluenceScores(actuals: Seq[Any]): Option[Seq[Seq[Double]]]

    Get the improvement (positive) or damage (negative) due to each training row on a prediction

    Get the improvement (positive) or damage (negative) due to each training row on a prediction

    actuals

    to assess the improvement or damage against

    returns

    Sequence (over predictions) of sequence (over training rows) of influence

    Definition Classes
    PredictionResult
  14. def getUncertainty(): Option[Seq[Any]]

    Get the uncertainty of the prediction

    Get the uncertainty of the prediction

    This is un-standardized by rescaling by the label's variance

    returns

    uncertainty of each prediction

    Definition Classes
    StandardizerPredictionPredictionResult
  15. def hashCode(): Int
    Definition Classes
    AnyRef → Any
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    @native()
  16. val intercept: Double
  17. final def isInstanceOf[T0]: Boolean
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  18. final def ne(arg0: AnyRef): Boolean
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  19. final def notify(): Unit
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    @native()
  20. final def notifyAll(): Unit
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    @native()
  21. val rescale: Double
  22. final def synchronized[T0](arg0: ⇒ T0): T0
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  23. def toString(): String
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  24. final def wait(): Unit
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  25. final def wait(arg0: Long, arg1: Int): Unit
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  26. final def wait(arg0: Long): Unit
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Inherited from PredictionResult[T]

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