A/B testing

A harmonious dance between intuition and foresight, A/B testing with predictive models is the process of comparing two or more predictive algorithms to determine which produces superior forecasts, enabling the continuous refinement and optimization of data-driven decision-making.

Example

A financial institution might conduct an A/B test to determine the most effective predictive model for assessing credit risk. They would create two or more models, each using a different set of features or algorithms, and then apply them to historical data. By comparing their performance in predicting credit defaults, the institution can identify the most accurate model and implement it to improve overall risk assessment and lending decisions.