Explanatory systems make the behavior of machine learning models more
transparent, but are often inconsistent. To quantify the differences between
explanatory systems, this paper presents the Shreyan Distance, a novel metric
based on the weighted difference between ranked feature importance lists
produced by such systems. This paper uses the Shreyan Distance to compare two
explanatory systems, SHAP and LIME, for both regression and classification
learning tasks. Because we find that the average Shreyan Distance varies
significantly between these two tasks, we conclude that consistency between
explainers not only depends on inherent properties of the explainers
themselves, but also the type of learning task. This paper further contributes
the XAISuite library, which integrates the Shreyan distance algorithm into
machine learning pipelines.

本文提出了一种新的度量标准，Shreyan 距离，用于量化解释性系统之间的差异，并使用该距离对 SHAP 和 LIME 这两种解释性系统在回归和分类学习任务中进行比较，结论是解释性系统的一致性不仅取决于解释性系统本身的属性，还取决于学习任务的类型。本文还介绍了 XAISuite 库，将 Shreyan 距离算法集成到机器学习流程中。

一种新的事后解释比较度量方法和应用

A novel post-hoc explanation comparison metric and applications

Explanatory systems make machine learning models more transparent. However,
they are often inconsistent. In order to quantify and isolate possible
scenarios leading to this discrepancy, this paper compares two explanatory
systems, SHAP and LIME, based on the correlation of their respective importance
scores using 14 machine learning models (7 regression and 7 classification) and
4 tabular datasets (2 regression and 2 classification). We make two novel
findings. Firstly, the magnitude of importance is not significant in
explanation consistency. The correlations between SHAP and LIME importance
scores for the most important features may or may not be more variable than the
correlation between SHAP and LIME importance scores averaged across all
features. Secondly, the similarity between SHAP and LIME importance scores
cannot predict model accuracy. In the process of our research, we construct an
open-source library, XAISuite, that unifies the process of training and
explaining models. Finally, this paper contributes a generalized framework to
better explain machine learning models and optimize their performance.

本文比较两种解释系统（SHAP 和 LIME）的解释一致性，使用 14 种机器学习模型和 4 个数据集进行了实验，并得出两个结论：解释重要性的大小不重要；SHAP 和 LIME 重要性得分的相似度不能预测模型的准确性。此外，本文构建了一个名为 XAISuite 的开源库，为训练和解释模型提供统一的流程，并提出了一个通用框架来更好地解释机器学习模型并优化它们的性能。