seniorSupervised Learning
What is A/B testing in supervised learning systems?
Updated May 17, 2026
Short answer
A/B testing compares two ML models in production to evaluate which performs better.
Deep explanation
A/B testing involves deploying two versions of a model (A and B) to different user groups and measuring performance using business or ML metrics. It ensures statistically valid comparison under real-world conditions. Metrics may include click-through rate, conversion rate, or latency.
Real-world example
Search engines testing ranking algorithms on different user groups.
Common mistakes
- Running A/B tests without sufficient sample size or statistical significance.
Follow-up questions
- What is statistical significance in A/B testing?
- What is multivariate testing?