Multiclass Classification#
Assigning each instance to one of three or more classes.
Important
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What it is#
Multiclass classification predicts one of more than two mutually exclusive classes — a handwritten digit (0–9), a species, a product category. It generalizes binary classification, and its models usually end in a softmax layer that outputs a probability over the K classes.
The evaluation twist#
Metrics built for two classes — ROC-AUC, precision, recall — have no direct multiclass definition, because “positive vs negative” is ambiguous with many classes. To use them, the problem is binarized (one class vs the others) and the per-class scores are averaged.
The two decompositions#
One-vs-Rest turns K classes into K binary problems (each class against the rest); One-vs-One compares every pair. Either produces a set of per-class or per-pair scores that a micro or macro average then collapses into a single number.
Theme: Classification & Averaging Metrics · All terminology
Hint
Mind map — connected ideas
One-vs-Rest (OvR) · Macro AUC · Micro AUC · Binary Classification · Softmax Function · Multiclass AUROC
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 Classification (insightful-data-lab.com).