Metrics precision score

Precision.Score

True positives over everything predicted into the class, reduced to one number by average.

public static double Score(ConfusionMatrix cm, Averaging average = Averaging.Binary, int posLabel = 1, ZeroDivision zeroDivision = ZeroDivision.Zero)
public static double Score(ReadOnlySpan<int> yTrue, ReadOnlySpan<int> yPred, Averaging average = Averaging.Binary, int posLabel = 1, ZeroDivision zeroDivision = ZeroDivision.Zero, ReadOnlySpan<int> labels = default, ReadOnlySpan<double> sampleWeight = default)

Parameters — cm is a matrix already counted, or pass yTrue and yPred. average is how the per-class scores are reduced, Averaging.Binary by default. posLabel is the class reported under Averaging.Binary. zeroDivision decides what comes back when nothing at all was predicted into the class. labels fixes the label set and its order, and sampleWeight weights the samples.

Returns — double in [0, 1], larger meaning fewer false alarms.

Exceptions — ArgumentNullException when cm is null; ArgumentException when Averaging.Binary is used on more than two classes, or posLabel does not occur; UndefinedMetricException when nothing was predicted into the class and zeroDivision is ZeroDivision.Throw.

Example — the filter flagged three messages as spam and two of them were.

using Lodestar.Metrics;

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

double precision = Precision.Score(yTrue, yPred);   // => 0.6666…

Remarks — report this when a false alarm is the expensive mistake: mail deleted that was not spam, a customer wrongly declined, a page taken down that was fine. It answers "when this thing fires, can I trust it", and says nothing at all about how much it missed.

Which is the trap, and it is not subtle: precision alone is trivially gamed. A model that flags exactly one sample, and is right about it, has a precision of 1.0. Precision is only a claim about a model when it is quoted next to a recall, or folded into F1.Score, which is why every report on this page carries both.

The undefined case is worth setting deliberately. A class nothing was predicted into has no precision — the denominator is zero — and by default that returns 0.0, which is scikit-learn's value and reads in a report as "terrible" rather than as "not asked". ZeroDivision.NaN keeps it out of a macro average honestly; ZeroDivision.Throw tells you rather than letting a silent zero drag a number down.

Applies to — net10.0, netstandard2.0.

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