case class StandardResidualHistogram(nBins: Int = 128, range: Double = 8.0, fitGaussian: Boolean = true, fitCauchy: Boolean = true) extends Visualization[Double] with Product with Serializable
Histogram of the error divided by the predicted uncertainty
Gaussian and Cauchy fits are preformed via quantiles:
- standard deviation is taken as the 68th percentile standard error
- gamma is taken as the 50th percentile standard error
- nBins
number of bins in the histogram
- range
of the horizontal axis, e.g. x \in [-range/2, range/2]
- fitGaussian
whether to fit and plot a Gaussian distribution
- fitCauchy
whether to fit and plot a Cauchy distribution
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StandardResidualHistogram(nBins: Int = 128, range: Double = 8.0, fitGaussian: Boolean = true, fitCauchy: Boolean = true)
- nBins
number of bins in the histogram
- range
of the horizontal axis, e.g. x \in [-range/2, range/2]
- fitGaussian
whether to fit and plot a Gaussian distribution
- fitCauchy
whether to fit and plot a Cauchy distribution
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- val fitCauchy: Boolean
- val fitGaussian: Boolean
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- val range: Double
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def
visualize(data: Iterable[(PredictionResult[Double], Seq[Double])]): CategoryChart
Produce a visualization, as a Chart, from predicted-vs-actual data
Produce a visualization, as a Chart, from predicted-vs-actual data
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- StandardResidualHistogram → Visualization
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