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io.citrine.lolo.validation

UncertaintyCorrelation

object UncertaintyCorrelation extends Merit[Double] with Product with Serializable

Measure of the correlation between the predicted uncertainty and error magnitude

This is expressed as a ratio of correlation coefficients. The numerator is the correlation coefficient of the predicted uncertainty and the actual error magnitude. The denominator is the correlation coefficient of the predicted uncertainty and the ideal error distribution. That is: let X be the predicted uncertainty and Y := N(0, x) be the ideal error distribution about each predicted uncertainty x. It is the correlation coefficient between X and Y In the absence of a closed form for that coefficient, it is model empirically by drawing from N(0, x) to produce an "ideal" error series from which the correlation coefficient can be estimated.

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Serializable, Serializable, Product, Equals, Merit[Double], AnyRef, Any
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  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
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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
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
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    @native() @throws( ... )
  6. def computeFromPredictedUncertaintyActual(pua: Seq[(Double, Double, Double)]): Double

    Covariance(X, Y) / Sqrt(Var(X) * Var(Y)), where X is predicted uncertainty and Y is magnitude of error

    Covariance(X, Y) / Sqrt(Var(X) * Var(Y)), where X is predicted uncertainty and Y is magnitude of error

    pua

    predicted, uncertainty, and actual

  7. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  8. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  9. def estimate(pva: Iterable[(PredictionResult[Double], Seq[Double])]): (Double, Double)

    Estimate the merit and the uncertainty in the merit over batches of predicted and ground-truth values

    Estimate the merit and the uncertainty in the merit over batches of predicted and ground-truth values

    pva

    predicted-vs-actual data as an iterable over PredictionResult and ground-truth tuples

    returns

    the estimate of the merit value and the uncertainty in that estimate

    Definition Classes
    Merit
  10. def evaluate(predictionResult: PredictionResult[Double], actual: Seq[Double]): Double

    Apply the figure of merti to a prediction result and set of ground-truth values

    Apply the figure of merti to a prediction result and set of ground-truth values

    returns

    the value of the figure of merit

    Definition Classes
    UncertaintyCorrelation → Merit
  11. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
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    @throws( classOf[java.lang.Throwable] )
  12. final def getClass(): Class[_]
    Definition Classes
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    @native()
  13. final def isInstanceOf[T0]: Boolean
    Definition Classes
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  14. final def ne(arg0: AnyRef): Boolean
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  15. final def notify(): Unit
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    @native()
  16. final def notifyAll(): Unit
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    @native()
  17. final def synchronized[T0](arg0: ⇒ T0): T0
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  18. final def wait(): Unit
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  19. final def wait(arg0: Long, arg1: Int): Unit
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  20. final def wait(arg0: Long): Unit
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Inherited from Serializable

Inherited from Serializable

Inherited from Product

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Inherited from Merit[Double]

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