Glossary › A/B Test
A/B Test
An experiment that randomly splits users into two groups to compare two interventions. The control group gets the current behavior; the treatment group gets the new one. Results are analyzed statistically to determine if the difference is real. Useful for validating that offline model improvements translate to business outcomes.
Where A/B Test shows up
All matching postsA/B Testing for ML Features: Measuring What Actually Matters
Offline AUC does not prove business value. A/B testing does. Here is how to design and analyze experiments that measure whether your ML improvement actually moves the metrics you care about.
Model Evaluation and Experiment Tracking
Evaluation is harder than training. This guide covers choosing the right metrics, understanding confidence intervals in model comparison, A/B testing discipline, and experiment tracking that makes ML work reproducible and auditable.