Explaining outliers occurrence and mechanism of their occurrence can be extremely important in a variety of domains. Malfunctions, frauds, threats, in addition to being correctly identified, oftentimes need a valid explanation in order to effectively perform actionable counteracts. The ever more widespread use of sophisticated Machine Learning approach to identify anomalies make such explanations more challenging. We present the Decision Tree Outlier Regressor (DTOR), a technique for producing rule-based explanations for individual data points by estimating anomaly scores generated by an anomaly detection model. This is accomplished by first applying a Decision Tree Regressor, which computes the estimation score, and then extracting the relative path associated with the data point score. Our results demonstrate the robustness of DTOR even in datasets with a large number of features. Additionally, in contrast to other rule-based approaches, the generated rules are consistently satisfied by the points to be explained. Furthermore, our evaluation metrics indicate comparable performance to Anchors in outlier explanation tasks, with reduced execution time.

翻译：解释异常的发生及其机制在各个领域都至关重要。故障、欺诈、威胁在被正确识别后，往往需要有效的解释以便采取可操作的对策。日益广泛使用的复杂机器学习方法在识别异常的同时，使此类解释更具挑战性。我们提出决策树异常回归器（DTOR），这是一种通过估计异常检测模型生成的异常分数来为单个数据点产生基于规则解释的技术。该技术首先应用决策树回归器计算估计分数，然后提取与数据点分数相关的路径。我们的结果表明，即使在高维数据集中，DTOR也展现出稳健性。此外，与其他基于规则的方法相比，生成的规则始终被待解释的数据点满足。评估指标显示，在异常解释任务中，DTOR的性能与Anchors相当，且执行时间更短。