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The explained fraction, one number per output column — d2_absolute_error_score(…, multioutput="raw_values").

public static double[] PerOutput(ReadOnlySpan<double> yTrue, ReadOnlySpan<double> yPred, int outputCount = 1, ReadOnlySpan<double> sampleWeight = default, ZeroDivision zeroDivision = ZeroDivision.NaN)

Parameters — yTrue and yPred are the true and predicted values, row-major. outputCount is how many outputs each row holds. sampleWeight is one weight per row. zeroDivision decides the answer for fewer than two samples; see D2AbsoluteError.Score.

Returns — a fresh double[] of outputCount entries, in column order. Each column gets its own median baseline, which is why the entries are not recoverable from the averaged score.

Exceptions — ArgumentException when a length disagrees with the shape, the input is empty, or it holds a non-finite value. ArgumentOutOfRangeException when outputCount is below one. UndefinedMetricException when there are fewer than two samples and zeroDivision is ZeroDivision.Throw.

Example — three samples over two outputs.

using Lodestar.Metrics;

double[] truth = [0.5, 1.0, 1.0, 1.0, 7.0, -6.0];
double[] predicted = [0.0, 2.0, -1.0, 2.0, 8.0, -5.0];

double[] columns = D2AbsoluteError.PerOutput(truth, predicted, outputCount: 2);
double first = columns[0];  // => 0.4615…

The two columns score 0.4615… and 0.5714…, whose plain mean is the 0.5164… that D2AbsoluteError.Score reports with no outputWeights.

Applies to — net10.0, netstandard2.0.

See also — D2AbsoluteError.Score, D2Pinball.PerOutput, the Python equivalence table.