RetrainOpt

RetrainOpt provides offline planning and online policies for when to retrain and how much data to use, balancing training cost and predictive loss. It implements algorithms from Automated Retraining for Cost-Optimal ML by Jun Yang and Michael Mathioudakis (ECML PKDD 2026).

The offline planner uses exact dynamic programming or surrogate-guided approximate search over a supplied timeline. The whole timeline, including evaluation data, is available to this planner. A plan’s evaluated cost is not a prospective test-set estimate when the same evaluation data were used to choose that plan.

The Base and Learned online policies support sequential retraining decisions from observed history.

Try the interactive scheduling example →

Change the cost of training and prediction errors to explore when and how much to retrain in a synthetic timeline.