Term tag: metrics

A/B Testing

A/B testing is the randomized experiment that decides whether a model, prompt, or feature change truly improves a live metric — or just moved on noise.

Calibration

Calibration means a model's confidence matches reality: of everything it calls 80% likely, about 80% should happen — a property separate from raw accuracy.

Class Imbalance

Why a fraud model can score 99% accuracy and catch nothing — the imbalance trap, why precision/recall/PR-AUC replace accuracy, and how to fix it.

Data Analysis

What the work consists of: framing a question, looking before aggregating, choosing a summary that does not lie, and turning a number into a decision.

Precision and Recall

Precision, recall, F1 and the confusion matrix worked through one imbalanced example — and why the model with the best accuracy can catch nothing at all.