unsupervised
Unsupervised Learning — Clustering, Anomalies, and Dimensionality Reduction
When the data has no target column, you're in unsupervised territory. Three things you can do with it:
- Cluster to find segments / groups
- Detect anomalies to find outliers / fraud / failures
- Reduce dimensions to visualize, denoise, or compress
Each comes with its own gotchas. The biggest one: there's no single "accuracy" metric, so you have to be deliberate about how you evaluate.
When to use this skill
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