Multilabel Precision#
Precision when each instance may carry several labels at once.
Important
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What it is#
Multilabel precision is precision when each sample can carry several labels at once — the labels are not mutually exclusive. Targets are an indicator matrix (cell [i, j] = 1 if sample i has label j), and precision is computed per label, each label a binary problem.
How it’s aggregated#
As with multiclass, per-label precisions are combined by micro, macro or weighted averaging — but multilabel adds a distinctive ‘samples’ average, which computes precision per instance (across that sample’s labels) and averages over samples.
Why it differs#
Unlike multiclass, where exactly one class is correct, multilabel labels co-occur, so a prediction can be partly right (some labels correct, others missed). The averaging choice — especially samples vs micro — decides whether you’re scoring per-label or per-example accuracy.
Theme: Classification & Averaging Metrics · All terminology
Hint
Mind map — connected ideas
Multiclass Precision · Per-class Precision (sometimes called class-wise precision) · One-vs-Rest (OvR) · Micro Averaging · Precision (a.k.a. Positive Predictive Value, PPV) · F1-score
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: Multilabel Precision (insightful-data-lab.com).