Multiclass Precision#
Precision aggregated across the classes of a multiclass problem.
Important
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What it is#
Multiclass precision is precision for a problem with more than two mutually exclusive classes. Since precision is defined on a binary positive/negative split, it is computed by treating the task as K one-vs-rest binary problems — the per-class precisions — then reduced to a single number.
How it’s reduced#
The per-class values are combined by an averaging scheme — micro (pool counts, majority-dominated), macro (equal weight per class), or weighted (by support). The choice determines whether rare classes are surfaced or hidden, so it must be stated with the score.
The caveat#
Because each class is scored against “the rest,” each binary split is imbalanced; reporting the per-class precisions guards against an average that looks good only because the majority class does.
Theme: Classification & Averaging Metrics · All terminology
Hint
Mind map — connected ideas
Per-class Precision (sometimes called class-wise precision) · Multilabel Precision · Multiclass Classification · Macro Averaging · Weighted Averaging · Precision (a.k.a. Positive Predictive Value, PPV)
Hint
More in Classification & Averaging Metrics
Accuracy · AUC (Area Under the Curve) · Average Precision (AP) · Binary Classification · Classification Probability · Discriminatory Power · F1-score · Gini Coefficient · Harmonic Mean · Log Loss (also called Logarithmic Loss or Cross-Entropy Loss) · Macro AUC · Macro AUROC (Macro-Averaged AUROC) · Macro Averaging · Macro F1
See also
Source article Adapted (context, re-expressed) in our own words from: Multiclass Precision (insightful-data-lab.com).