Lodestar.Metrics 0.3.0. This page is frozen at that release. Read the current documentation for what main says now. A link to a decision or a migration page follows main, and leaves the archive.

One number for the whole prediction: the share of the truth's variance the residuals do not carry.

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

Parameters — yTrue and yPred are the true and predicted values, the same length and row-major when there is more than one output. outputCount is how many outputs each row holds, 1 by default. sampleWeight weights the rows. outputWeights weights the outputs when the per-output scores are reduced; omit it for a plain mean. forceFinite clamps the zero-variance case to 1 or 0 rather than letting it be nan or -inf.

Returns — double at most 1: 1 for a prediction that tracks the truth exactly up to a constant, 0 for one no better than the mean, and negative for one that is worse.

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 — a prediction that is right about every change and wrong by exactly 1 every time. R2.Score on the same data is -0.5.

using Lodestar.Metrics;

double[] yTrue = [1.0, 2.0, 3.0];
double[] yPred = [2.0, 3.0, 4.0];

double explained = ExplainedVariance.Score(yTrue, yPred);   // => 1

Remarks — one term separates this from R2, and the example above is it: the residuals are centred on their own mean before being squared, so a uniform bias costs nothing here and costs R2 everything. That makes this the right metric when the offset is going to be calibrated away later — a sensor with an unknown zero, a forecast you will recentre — and the wrong one when the offset is the error you are trying to measure.

Because a bias is free, this is always at least as large as R2 on the same data, and the gap between the two is exactly the bias. Reporting both is a cheap and unusually informative pair: equal numbers mean the model is unbiased, and a wide gap says the shape is right and the level is not.

The trap is quoting it alone as though it were R2. A model that predicts y + 1000 scores 1 here and is useless. If a reader is going to see one number, R2 is the safer one.

Unlike R2, this takes no ZeroDivision: it has no fewer-than-two-samples case to route, so ExplainedVariance.Score([3.0], [5.0]) is 1.0 where R2.Score on the same input is NaN. The reasoning is in decision 0026.

Applies to — net10.0, netstandard2.0.

See also — ExplainedVariance.PerOutput, ExplainedVariance.VarianceWeighted, R2.Score, decision 0026, the Python equivalence table.