c

io.citrine.lolo.transformers

StandardizerPrediction

class StandardizerPrediction[T] extends PredictionResult[T]

Prediction that wraps the base prediction next to the transformation

T

type of prediction

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

  1. new StandardizerPrediction(baseResult: PredictionResult[T], outputTrans: Option[Standardization], inputTrans: Seq[Option[Standardization]])

    baseResult

    result of applying model to standardized inputs

    outputTrans

    optional transformation (rescale, offset) of output prediction

    inputTrans

    sequence of optional transformations (rescale, offset) of inputs

Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##: Int
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  3. final def ==(arg0: Any): Boolean
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  4. final def asInstanceOf[T0]: T0
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  5. def clone(): AnyRef
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    protected[lang]
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    @throws(classOf[java.lang.CloneNotSupportedException]) @native()
  6. final def eq(arg0: AnyRef): Boolean
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  7. def equals(arg0: AnyRef): Boolean
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  8. def finalize(): Unit
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    @throws(classOf[java.lang.Throwable])
  9. final def getClass(): Class[_ <: AnyRef]
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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
    StandardizerPrediction → PredictionResult
  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
    StandardizerPrediction → PredictionResult
  12. def getImportanceScores(): Option[Seq[Seq[Double]]]

    Get the training row scores for each prediction

    Get the training row scores for each prediction

    returns

    sequence (over predictions) of sequence (over training rows) of importances

    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(includeNoise: Boolean = true): 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
    StandardizerPrediction → PredictionResult
  15. def hashCode(): Int
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    @native()
  16. final def isInstanceOf[T0]: Boolean
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  17. final def ne(arg0: AnyRef): Boolean
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  18. final def notify(): Unit
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  19. final def notifyAll(): Unit
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  20. val outputRescale: Double
  21. final def synchronized[T0](arg0: => T0): T0
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  22. def toString(): String
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  23. final def wait(): Unit
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  24. final def wait(arg0: Long, arg1: Int): Unit
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  25. final def wait(arg0: Long): Unit
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Inherited from PredictionResult[T]

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