Explaining predictions made by complex machine learning models helps users understand and accept the predicted outputs with confidence. Instance-based explanation provides such help by identifying relevant instances as evidence to support a model's prediction result. To find relevant instances, several relevance metrics have been proposed. In this study, we ask the following research question: "Do the metrics actually work in practice?" To address this question, we propose two sanity check criteria that valid metrics should pass, and two additional criteria to evaluate the practical utility of the metrics. All criteria are designed in terms of whether the metric can pick up instances of desirable properties that the users expect in practice. Through experiments, we obtained two insights. First, some popular relevance metrics do not pass sanity check criteria. Second, some metrics based on cosine similarity perform better than other metrics, which would be recommended choices in practice. We also analyze why some metrics are successful and why some are not. We expect our insights to help further researches such as developing better explanation methods or designing new evaluation criteria.

本研究探讨了几种用来解释机器学习模型预测结果的相关性评估指标，并通过三项测试，发现利用损失函数梯度的余弦相似度表现最佳，可作为实践中的推荐选择。同时，研究者还发现某些指标表现不佳，并分析了其失败原因，期望能帮助从业者选择合适的相关性评估指标，同时推动更好的指标研究。

基于相似性的解释评估