Module 7: clustering and learning paradigms

{
  "module": 7,
  "dataset": "UCI red wine chemistry",
  "selected_k": 2,
  "selection": "validation silhouette after training-fitted scaling",
  "test_observations": 243,
  "inertia_train": 10008.78683950145,
  "quality_used_for_fit_or_selection": false
}

Cluster numbers are arbitrary IDs, not ranks or taste classes. Different scales, seeds, and shapes can change the partition.

Silhouette is an internal geometry criterion, not ground-truth accuracy or an operational benefit measure.

This static observational dataset supports supervised/unsupervised comparison. It has no action/reward interaction needed to validate a reinforcement-learning policy.