提出新方法精准界定模型适用范围，提升预测可靠性。
Comparative Evaluation of Applicability Domain Definition Methods for Regression Models
- 基于非确定性贝叶斯神经网络定义模型适用域
- 在5个数据集上对比8种方法，新方法准确率最优
- 适合关注模型可信预测边界的科研与工业用户
适用域指预测模型可可靠、准确预测的数据范围，超出该范围使用模型可能导致错误结果。准确界定模型适用域是保障新预测可靠性的重要前提。然而，由于缺乏明确统一的定义或度量标准，适用域的界定仍具挑战性。本文旨在使适用域更可量化、更实用。我们对七种回归模型在五个不同数据集上应用了八种适用域检测技术，并通过验证框架进行性能评估。同时，提出一种基于非确定性贝叶斯神经网络的新方法。实验结果表明，该方法在界定适用域方面优于现有方法，展现出显著潜力。
原文摘要 · Abstract (English)
The applicability domain refers to the range of data for which the prediction of the predictive model is expected to be reliable and accurate and using a model outside its applicability domain can lead to incorrect results. The ability to define the regions in data space where a predictive model can be safely used is a necessary condition for having safer and more reliable predictions to assure the reliability of new predictions. However, defining the applicability domain of a model is a challenging problem, as there is no clear and universal definition or metric for it. This work aims to make the applicability domain more quantifiable and pragmatic. Eight applicability domain detection techniques were applied to seven regression models, trained on five different datasets, and their performance was benchmarked using a validation framework. We also propose a novel approach based on non-deterministic Bayesian neural networks to define the applicability domain of the model. Our method exhibited superior accuracy in defining the Applicability Domain compared to previous methods, highlighting its potential in this regard.
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