'Cluster then classify' — when does it help?
mediumAnswer
- Add cluster label as an extra feature to a supervised model.
- Sometimes lifts performance on data with strong latent groups (customer segments, region clusters) — the label encodes a non-linear grouping the model wouldn't easily learn.
- But: usually replaces with target-encoded categoricals and interaction features that a GBDT can learn directly.
- Test with CV — often marginal or negative for modern tree models.
Check yourself — multiple choice
- Always helps
- Add cluster label as feature; occasional lift for strong latent groups; modern GBDTs often learn equivalent features → validate via CV
- Random
- Never helps
Cluster-then-classify: sometimes helps as extra feature; test via CV.
#clustering#applications
Practise Unsupervised Learning
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