A retailer wants to discover natural customer segments from purchase data that has NO labels or target column. Which learning paradigm fits?
A.Supervised learning
Needs labeled examples — you have none.
B.Unsupervised learning
Correct. Clustering unlabeled data is the textbook case.
C.Reinforcement learning
Reward-signal learning — for sequential decisions, not segmentation.
D.Fine-tuning
Fine-tuning adapts a pretrained model, still needs task data.
Why this answer
Unsupervised learning finds structure (clusters, associations, anomalies) in unlabeled data — customer segmentation is the canonical use case. Supervised requires labeled targets; reinforcement learns via rewards from actions.
Correct answer: B · Last reviewed 2026-11-02
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