Prediction problems often admit competing models that perform almost equally
well. This effect challenges key assumptions in machine learning when competing
models assign conflicting predictions. In this paper, we define predictive
multiplicity as the ability of a prediction problem to admit competing models
with conflicting predictions. We introduce formal measures to evaluate the
severity of predictive multiplicity and develop integer programming tools to
compute them exactly for linear classification problems. We apply our tools to
measure predictive multiplicity in recidivism prediction problems. Our results
show that real-world datasets may admit competing models that assign wildly
conflicting predictions, and motivate the need to measure and report predictive
multiplicity in model development.

本研究旨在解决机器学习中不同模型之间预测结果冲突的问题，定义了预测重复性作为预测问题接受有多个竞争模型的能力，并引入形式化的度量方式和整数规划工具来计算它们，应用于反复犯罪预测问题中，结果显示现实世界的数据集会接受互相冲突的模型，提出了需要在模型评估中报告预测重复性的建议。