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The share of samples whose predicted label equals the true one.

public static double Score(ReadOnlySpan<int> yTrue, ReadOnlySpan<int> yPred, bool normalize = true, ReadOnlySpan<double> sampleWeight = default)
public static double Score(ConfusionMatrix cm, bool normalize = true)

Parameters — yTrue and yPred are the true and predicted labels, one per sample and the same length. cm is the alternative to both: a matrix already counted, which is what you pass when several metrics are being read off one dataset. normalize chooses between the fraction (true, the default) and the raw weight of the correct samples (false). sampleWeight gives each sample its own weight; omit it and every sample counts 1.

Returns — double in [0, 1] when normalize is true, 1 meaning every sample was right. With normalize: false it is a count instead — a weight, not a fraction, and unbounded.

Exceptions — ArgumentException when the two label spans disagree in length or are empty; ArgumentNullException when cm is null. A sampleWeight holding NaN or an infinity is refused with "Input sample_weight contains NaN." or its infinity counterpart, and one that is zero throughout with "Sample weights must contain at least one non-zero number." — both ArgumentException naming sampleWeight, as scikit-learn's _check_sample_weight refuses them. Weights that merely sum to zero are refused too, with numpy's "Weights sum to zero, can't be normalized." — only while normalize is true, since the count never divides.

Example — four spam messages and four legitimate ones; the filter caught two of the four and raised one false alarm.

using Lodestar.Metrics;

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

double share = Accuracy.Score(yTrue, yPred);   // => 0.625

Remarks — this is the right metric when the classes are roughly balanced and every mistake costs about the same. Both conditions matter, and the first one is where people get hurt.

The trap has a number attached. Take ten samples of which two belong to the class you care about, predict the majority class for all ten, and this returns 0.8 while BalancedAccuracy.Score returns 0.5 — the score a coin gets. Accuracy is a weighted average of the per-class recalls in which each class is weighted by how common it is, so a class that is 2% of the data moves it by at most 0.02. If your positive class is rare, this number is measuring the negative class and telling you about it.

The ConfusionMatrix overload has one behaviour of its own worth knowing. It is accuracy over the samples the matrix kept: a matrix built with an explicit labels subset drops every sample whose true or predicted label falls outside that subset, so on a three-class problem restricted to two labels this can read 0.75 where the same data scored over every sample reads 0.7142…. That is not a bug in either number — they are answers to different questions — but only the span overload matches accuracy_score.

Applies to — net10.0, netstandard2.0.

See also — BalancedAccuracy.Score, ConfusionMatrix.Compute, ClassificationReport.Compute, the Python equivalence table.