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One score per output, unreduced.

public static double[] PerOutput(ReadOnlySpan<double> yTrue, ReadOnlySpan<double> yPred, int outputCount = 1, ReadOnlySpan<double> sampleWeight = default, bool forceFinite = true)

Parameters — yTrue and yPred are the true and predicted values, row-major. outputCount is how many outputs each row holds, sampleWeight weights the rows, and forceFinite clamps the zero-variance case.

Returns — a fresh double[] of outputCount entries, in column order.

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.

Example — three samples, two outputs; the second output is predicted with a constant offset and therefore scores perfectly here.

using Lodestar.Metrics;

double[] yTrue = [0.5, 1.0, -1.0, 1.0, 7.0, -6.0];
double[] yPred = [0.0, 2.0, -1.0, 2.0, 8.0, -5.0];

double[] perOutput = ExplainedVariance.PerOutput(yTrue, yPred, outputCount: 2);
double first = perOutput[0];    // => 0.9677…
double second = perOutput[1];   // => 1

Remarks — this is scikit-learn's multioutput="raw_values", and the reason to want it is that a mean over outputs hides which output is failing. The array is in column order: entry i is the score of the value at offset i of every row.

The trap is the layout of the input rather than of the output. yTrue and yPred are row-major — one sample's outputs are contiguous — which is the transpose of a column-per-output table. Passing a column-major array with the right outputCount produces numbers rather than an error, and they are meaningless.

Applies to — net10.0, netstandard2.0.

See also — ExplainedVariance.Score, ExplainedVariance.VarianceWeighted, R2.PerOutput, the Python equivalence table.