multilabel-classification
Multilabel Classification with XGBoost (Done Right)
Multilabel ≠ multiclass. Multiclass picks one class from N. Multilabel predicts any subset of N labels — each row can have zero, one, or many labels on simultaneously. The metrics, the model wrapping, and the failure modes are all different.
For tabular multilabel, default to XGBoost wrapped in
MultiOutputClassifier: it fits one independent XGBoost model per
label. Simple, fast, and competitive. Switch to ClassifierChain only
when labels are correlated and the ordering is meaningful.
When to use this skill
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