A/B testing

A/B testing with predictive models compares 2 or more algorithms on the same data. You define the metrics upfront, run each variant under the same conditions, and choose the model with better predictions and lower error.

Example

A financial institution runs an A/B test to choose the most accurate model for credit risk. It builds several models with different features or algorithms, then scores the same historical data. The team compares accuracy and error, selects the strongest performer, and deploys it to improve risk assessment and lending decisions.