Metrics fbeta perclass

Development build. This page describes main, not a released package. The latest published Lodestar.Metrics is 0.3.0 — read its documentation.

FBeta.PerClass

F-beta for every class, in label order.

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

Parameters — cm is a matrix already counted, or pass yTrue and yPred. beta is the weight of recall relative to precision. zeroDivision decides an undefined per-class score, labels fixes the label set and its order, and sampleWeight weights the samples.

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

Exceptions — ArgumentOutOfRangeException when beta is negative, NaN or infinite; ArgumentNullException when cm is null; ArgumentException when the label spans disagree in length or are empty.

Example — both classes at beta = 2.

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 = FBeta.PerClass(yTrue, yPred, 2.0);
double ham = perClass[0];    // => 0.7142…
double spam = perClass[1];   // => 0.5263…

Remarks — the per-class form exists for the same reason F1.PerClass does: to see which class a macro average is hiding. It is worth a moment's thought before using it, though, because beta weights recall over precision for every class at once, and the asymmetry that justified beta was usually about one class in particular.

The trap is the arithmetic behind the scenes rather than in the result. beta is applied by substituting the true positives, the predicted count and the support algebraically rather than by computing precision and recall and combining them, which is what keeps the answer exact at the edges where one of the two is undefined — decision 0032 has the derivation. Nothing about the call changes; it is the reason the undefined cases here agree with scikit-learn rather than approximately agreeing.

Applies to — net10.0, netstandard2.0.

See also — FBeta.Score, F1.PerClass, ClassificationReport.Compute, decision 0032, the Python equivalence table.