case 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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- 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
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predicted, uncertainty, and actual
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- def estimate(pva: Iterable[(PredictionResult[Double], Seq[Double])], rng: Random = Random): (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
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predicted-vs-actual data as an iterable over PredictionResult and ground-truth tuples
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the estimate of the merit value and the uncertainty in that estimate
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- def evaluate(predictionResult: PredictionResult[Double], actual: Seq[Double], rng: Random = Random): 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
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the value of the figure of merit
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- UncertaintyCorrelation → Merit
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