# Model Selection in Utility-Maximizing Binary Prediction

## TL;DR

This paper introduces a utility-maximizing prediction rule (UMPR) that reduces overfitting in binary classification, providing theoretical bounds and demonstrating improved utility over standard estimators under misspecification.

## Contribution

It develops a new UMPR method with theoretical guarantees to mitigate overfitting in utility-based binary prediction models.

## Key findings

- UMPR achieves higher generalized expected utility under misspecification.
- Non-asymptotic bounds are established for the difference in utility.
- Simulation results confirm UMPR's superior performance over common estimators.

## Abstract

The maximum utility estimation proposed by Elliott and Lieli (2013) can be viewed as cost-sensitive binary classification; thus, its in-sample overfitting issue is similar to that of perceptron learning. A utility-maximizing prediction rule (UMPR) is constructed to alleviate the in-sample overfitting of the maximum utility estimation. We establish non-asymptotic upper bounds on the difference between the maximal expected utility and the generalized expected utility of the UMPR. Simulation results show that the UMPR with an appropriate data-dependent penalty achieves larger generalized expected utility than common estimators in the binary classification if the conditional probability of the binary outcome is misspecified.