提出误差敏感度分析方法，帮模型识别哪些特征出错影响最大。
Measuring the Sensitivity of Classification Models with the Error Sensitivity Profile
- 通过误差敏感度轮廓量化单个或多个特征错误对模型的影响
- 实验证明性能下降无法仅靠目标变量相关性预测
- 适合数据清洗优先级判断和模型鲁棒性评估
训练数据质量对机器学习模型性能至关重要。本文提出误差敏感度轮廓（Error Sensitivity Profile, ESP），用于量化模型性能在单个或多个特征出现错误时的敏感程度。借助ESP，可依据最可能影响模型表现的错误类型和特征，优先开展数据清洗工作。为此，我们开发了名为\dirty的集成工具套件以支持该指标计算。我们在两个广泛使用的数据集上，使用14种分类模型进行了大规模实验，结果表明：模型性能的下降并不总能通过特征与目标变量之间的简单相关性来预测。
原文摘要 · Abstract (English)
The quality of training data is critical to the performance of machine learning models. In this paper, the Error Sensitivity Profile (ESP) is proposed. It quantifies the sensitivity of model performance to errors in a single feature or in multiple features. By leveraging ESP, data-cleaning efforts can be prioritized based on error types and features most likely to affect model performance. To support the computation of this metric, an integrated suite of tools, called \dirty, is created. We conduct an extensive experimental study on two widely used datasets using 14 classification models, revealing that performance degradation is not always predictable from simple correlations with the target variable.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。