使用主动学习量化局部模型有效性
Quantifying Local Model Validity using Active Learning
AI总结：
针对局部模型有效性评估需额外数据且全局指标不敏感的问题，本文提出用主动学习学习模型误差，以较少数据获得具有判别力的局部有效性估计。
摘要：
机器学习模型在现实世界中的应用通常受到法律或政策法规的约束。其中部分法规要求确保模型的有效性，即近似误差小于某个阈值。全局指标通常过于不敏感，无法判断某一具体预测的有效性，而评估局部有效性成本很高，因为这需要收集额外数据。我们提出学习模型误差以获得局部有效性估计，同时通过主动学习减少所需数据量。利用模型验证基准，我们提供的实证证据表明，所提方法能够使用相对较少的数据得到具有充分判别特性的误差模型。此外，与替代方法相比，该方法对有效性边界的局部变化表现出更高的敏感性。
英文摘要：
Real-world applications of machine learning models are often subject to legal or policy-based regulations. Some of these regulations require ensuring the validity of the model, i.e., the approximation error being smaller than a threshold. A global metric is generally too insensitive to determine the validity of a specific prediction, whereas evaluating local validity is costly since it requires gathering additional data.We propose learning the model error to acquire a local validity estimate while reducing the amount of required data through active learning. Using model validation benchmarks, we provide empirical evidence that the proposed method can lead to an error model with sufficient discriminative properties using a relatively small amount of data. Furthermore, an increased sensitivity to local changes of the validity bounds compared to alternative approaches is demonstrated.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
