3 docs tagged with "regression"

A trace tells you one run went wrong. A dataset lets you check whether a prompt or model change made things better or worse across many runs at once — the LLM equivalent of a regression test suite.

Regression metrics all try to say "how close were the predictions" in a single number, but they disagree about what "close" means — and that disagreement matters exactly when outliers are present or when comparing models. Knowing which metric lies to you in which situation is the actual skill.

Every model in this knowledge base is measured against linear regression, and for good reason: it's the only widely-used model whose optimum you can write down in one line, no iteration required. Understanding exactly why that's possible — and exactly when it stops being possible — is the fastest way to understand the rest of classical ML.