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F1 for every class, in label order.

public static double[] PerClass(ConfusionMatrix cm, ZeroDivision zeroDivision = ZeroDivision.Zero)
public static double[] PerClass(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 what an undefined per-class score becomes. labels fixes the label set and its order, and sampleWeight weights the samples.

Returns — a fresh double[], one entry per label, in the same order as the matrix's Labels.

Exceptions — ArgumentNullException when cm is null; ArgumentException when the label spans disagree in length or are empty. 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.

Example — both classes of the spam filter at once.

using Lodestar.Metrics;

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

double[] perClass = F1.PerClass(yTrue, yPred);
double ham = perClass[0];    // => 0.6666…
double spam = perClass[1];   // => 0.5714…

Remarks — this is scikit-learn's average=None, and it is a separate method rather than an Averaging member because it returns an array where the others return a scalar; an enum cannot change a return type. Reach for it when you want to see which class is dragging a macro average down, which is the first question a bad macro score raises.

The trap is index versus label. The array is positional in the matrix's label order, so on labels [10, 20, 30] the score for class 20 is at index 1, not at index 20. Read cm.Labels[i], or use ClassificationReport.Compute, whose ClassRow carries the label with the score.

If you want all three of precision, recall and F1 per class, ClassificationReport.Compute computes them in one pass over one matrix instead of three.

Applies to — net10.0, netstandard2.0.

See also — F1.Score, Precision.PerClass, Recall.PerClass, ClassificationReport.Compute, the Python equivalence table.