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The correlation between the predicted and the true labels, in [-1, 1].

public static double Score(ConfusionMatrix cm, ZeroDivision zeroDivision = ZeroDivision.Zero)
public static double Score(ReadOnlySpan<int> yTrue, ReadOnlySpan<int> yPred, ZeroDivision zeroDivision = ZeroDivision.Zero, ReadOnlySpan<int> labels = default, ReadOnlySpan<double> sampleWeight = default)

Parameters — cm is a matrix already counted, or pass yTrue and yPred. zeroDivision decides the answer when the denominator collapses. labels fixes the label set and its order, and sampleWeight weights the samples.

Returns — double in [-1, 1]: 1 for a perfect prediction, 0 for one no better than chance, and negative when the prediction is systematically inverted.

Exceptions — ArgumentNullException when cm is null; ArgumentException when the label spans disagree in length or are empty, or sampleWeight holds a non-finite value or is zero throughout; UndefinedMetricException when the correlation is undefined and zeroDivision is ZeroDivision.Throw.

Example — the spam filter, which F1 scores 0.5714… and this scores far lower.

using Lodestar.Metrics;

int[] yTrue = [1, 1, 1, 1, 0, 0, 0, 0];
int[] yPred = [1, 1, 0, 0, 1, 0, 0, 0];

double correlation = MatthewsCorrelation.Score(yTrue, yPred);   // => 0.2581…

Remarks — this is the metric to reach for when you want a single number that cannot be gamed by predicting the majority class. Unlike F1 it reads all four cells, so a model that says "no" to everything scores 0 here whatever the class balance is, and unlike accuracy it does not drift upward as the majority grows. It is symmetric in the two classes as well: swapping which one you call positive leaves the number alone, which F1 does not.

The [-1, 1] range carries information the others cannot. A negative score means the model is anti-correlated with the truth — reliably wrong, which is a different failure from being random and usually points at an inverted label somewhere.

Two things about the undefined case. The denominator collapses when one input is constant — a truth with only one class, or a prediction with only one class — and scikit-learn hard-codes 0.0 there. This returns the same value by default, and additionally lets you ask to be told instead, with ZeroDivision.Throw; that is an extension beyond parity, not a divergence in value. The ConfusionMatrix overload scores only the classes the matrix holds, and matthews_corrcoef has no labels parameter, so for a restricted matrix there is no reference value to compare against.

The trap is reading it as a percentage. 0.2581… is not "26% right"; correlations are not shares, and a Matthews score of 0.3 is a considerably better model than the number looks next to an accuracy of 0.625 on the same data.

Applies to — net10.0, netstandard2.0.

See also — CohenKappa.Score, BalancedAccuracy.Score, F1.Score, the Python equivalence table.