发表机构
University of Amsterdam（阿姆斯特丹大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对金融领域可解释分类器可读性差的问题，本文提出从单方程可解释分类器逐步简化为可读规则的方法，经四组金融数据集验证，该方法可在损失较小的情况下生成有效规则，且部分损失可从原始模型预判。
AI 中文摘要
在金融等受监管领域，无法解释的模型无法部署，但许多可解释分类器却因生成包含数十个特征的公式而失去作用，这类公式连监管机构都无法读懂。我们采取反向思路：从一个用输入特征表示的单方程可解释分类器出发，逐步将其简化为更可读的形式，包括剪枝单项式、定向if-then规则，以及金融领域已在使用的整数评分卡和计数表。由于该方程本身就是预测模型而非事后解释，我们可直接量化每次简化造成的损失。在四个金融数据集上，我们发现剪枝几乎无代价，且保真度可能比预测性能下降更快，这使得更简单的规则无需忠实复现原始模型即可成为有效的分类器。人工评估显示，简化提升了感知可读性，而对不同表示形式的偏好因专业背景而异。除了实证测量这些损失，我们还表明部分损失可从原始模型预判：我们推导了剪枝造成变化的边界，并预测仅保留每个特征效应方向的规则能在多大程度上忠实保留原始排序。
英文摘要
In regulated domains such as finance, a model that cannot be explained cannot be deployed, yet many interpretable classifiers defeat their own purpose by producing formulas with dozens of features that no regulator could read. We take the reverse direction. Starting from an interpretable classifier expressed as a single equation over the input features, we progressively simplify it into more readable forms, including a pruned monomial, a directional if--then rule, and the integer scorecards and tallies that finance already deploys. Because the equation is itself the predictive model rather than a post-hoc explanation we can directly quantify what is lost under each simplification. Across four financial datasets, we find that pruning is nearly free and that fidelity can erode faster than predictive performance, allowing simpler rules to remain effective classifiers without faithfully reproducing the original model. A human assessment shows that simplification improves perceived readability, while preferences for different representations vary by professional background. Beyond measuring these losses empirically, we show that some can be anticipated from the original model: we derive a bound on the change caused by pruning and predict how faithfully a rule retaining only the direction of each feature's effect preserves the original ranking.
Comments8 pages, 3 figures. Submitted to ICAIF 2026
