I examine the construction and evaluation of machine learning (ML) binary classification models. These models are increasingly used for societal applications such as classifying patients into two categories according to the presence or absence of a certain disease like cancer and heart disease. I argue that the construction of ML (binary) classification models involves an optimisation process aiming at the minimization of the inductive risk associated with the intended uses of these models. I also argue that the construction of these models is underdetermined by the available data, and that this makes it necessary for ML modellers to make social value judgments in determining the error costs (associated with misclassifications) used in ML optimization. I thus suggest that the assessment of the inductive risk with respect to the social values of the intended users is an integral part of the construction and evaluation of ML classification models. I also discuss the implications of this conclusion for the philosophical debate concerning inductive risk.

我研究了机器学习 (ML) 二元分类模型的构建和评估。这些模型越来越多地用于社会应用，例如根据是否存在某种疾病（如癌症和心脏病）将患者分为两类。我认为，ML（二进制）分类模型的构建涉及一个优化过程，旨在最小化与这些模型的预期用途相关的归纳风险。我还认为，可用数据无法确定这些模型的构建，这使得 ML 建模者有必要在确定 ML 优化中使用的错误成本（与错误分类相关）时做出社会价值判断。因此，我建议针对目标用户的社会价值评估归纳风险是构建和评估 ML 分类模型的一个组成部分。我还讨论了这一结论对有关归纳风险的哲学辩论的影响。