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