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Research questionHow can teams estimate whether multiclass accuracy gains reduce optimization regret before building a model?In predict-then-optimize settings, classifier errors affect the quality of the resulting optimization decision, so predictive accuracy alone may not indicate practical value. This relationship is harder to estimate for categorical parameters when multiple misclassifications interact and repeated downstream optimization solves are costly.
AI
Machine Learning
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Statistical Machine Learning
Latest papersRecent research connected to this question, newest first.Simulating Classification Models for Ex-Ante Evaluation of Predict-Then-Optimize MethodsThe source concerns optimization problems with categorical uncertain parameters and multiclass predictions at prescribed performance levels. Its computational experiments show that simulated predictions reproduce target classification performance and that a first-order approximation based on individual misclassifications can closely match error-to-regret mappings for some problems, with reduced accuracy when misclassification interactions matter.research paper · Sep 2, 2026
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