Unsupervised Learning
Finding structure in data nobody labelled — useful, underrated, and much harder to know if you got right.
When not to use it
- When you have labels. If you know the categories, supervised learning is more accurate and you can actually measure it.
- When you need a defensible answer. "The algorithm found these groups" is not a justification a regulator or a board will accept without a domain expert vouching for them.
- Expecting the algorithm to tell you what matters. It finds structure under its own assumptions. You supply the meaning.
Reach for something else instead
- Supervised learning if labelling even a few hundred examples is feasible. It usually is, and it's usually worth it.
- Self-supervised methods when you want representations from unlabelled data — this is what actually works at scale now.
- Just looking at the data. Sorting, cross-tabbing, and plotting answers more questions than people expect, and it's honest about what it found.
Sources & further reading
- Hastie, Tibshirani & Friedman, The Elements of Statistical Learning, ch. 14 — the reference treatment, free from the authors.
- Wattenberg, Viégas & Johnson (2016), How to Use t-SNE Effectively — why the plots mislead, shown interactively.
- von Luxburg, Williamson & Guyon (2012), Clustering: Science or Art? — the evaluation problem, stated honestly.
Primary sources, listed so you can check the claims on this page rather than take them on trust.
Where people go wrong
- Not scaling features, then discovering the clusters are entirely about whichever column had the biggest numbers.
- Reading distances and cluster sizes off a t-SNE or UMAP plot. Neither is meaningful; the plot is a projection, not a map.
- Accepting the clusters because the silhouette score was good. That measures agreement with the algorithm's assumptions, not truth.
At a glance
FieldMachine Learning
Core ideastructure without labels
Usessegmentation, anomalies, compression
Hard partknowing if it's real
DifficultyIntermediate
Flashcards for this concept
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Often compared with
Unsupervised vs. supervised — asking what's in the data vs. teaching it what you already know.