lesson 3 of 4 · embeddings as features, not magiclesson 3 of 4 · embeddings as features, not magiclesson 3 of 4 · embeddings as features, not magiclesson 3 of 4 · embeddings as features, not magiclesson 3 of 4 · embeddings as features, not magiclesson 3 of 4 · embeddings as features, not magiclesson 3 of 4 · embeddings as features, not magiclesson 3 of 4 · embeddings as features, not magiclesson 3 of 4 · embeddings as features, not magiclesson 3 of 4 · embeddings as features, not magiclesson 3 of 4 · embeddings as features, not magic
Checkpoint
One last thing before we move on. pass this to mark the lesson done, or skip and keep moving. hop to the next when you're ready.
Evaluate the zero-training classifier like a real model. Write classify(vec) (nearest anchor by cosine), run it over the five labeled docs, print one line per doc — f"{text:<22} pred={pred:<8} {mark}" where mark is "ok" or "MISS" — and finish with f"accuracy: {...:.0%}". Embeddings as features means the whole chapter-39 evaluation playbook applies to them unchanged.