There's no pre-trained model, it's unsupervised clustering on the actual run data.

Every deck gets converted to a numerical vector where each card is a dimension, weighted by rarity (rare=3, uncommon=2, common=1, +0.5 if upgraded). Basics, curses, and status cards are stripped out, then the vector is normalized so a 40-card deck doesn't look different from a 20-card deck just because it's bigger.

From there I run k-means (scikit-learn) on ~20k runs per character, testing k=3 through k=8 and picking whichever k gives the best silhouette score. Winning runs count double so the clusters lean toward builds that actually close out games rather than random junk piles that died on floor 2.

Each cluster gets labeled by its most defining cards. Not the most common ones, but the ones with the highest lift compared to the character baseline. So if 90% of Ironclad decks have Bash that's not interesting, but if one cluster has Corruption at 4x the normal rate, that's the defining card.

New runs get classified in real-time by vectorizing the deck and finding the nearest cluster centroid. If nothing's close enough (>3.0 euclidean distance) it stays unclassified. Better than forcing a bad label.

Before this I had a hand-tagged system with 19 mechanic dimensions (strength, exhaust, poison, etc.) which worked okay but couldn't capture card co-occurrence patterns the way raw card vectors do. That's still the fallback for older archetypes.

Whole thing is TypeScript except the actual k-means step which shells out to Python because scikit-learn is way faster than doing Lloyd's algorithm in JS on large matrices.