Predictive models often degrade in performance due to evolving data distributions, a phenomenon known as data drift. Among its forms, concept drift, where the relationship between explanatory variables and the response variable changes, is particularly challenging to detect and adapt to. Traditional drift detection methods often rely on metrics such as accuracy or marginal variable distributions, which may fail to capture subtle but important conceptual changes. This paper proposes a novel method, Profile Drift Detection (PDD), which enables both the detection of concept drift and an enhanced understanding of its underlying causes by leveraging an explainable AI tool: Partial Dependence Profiles (PDPs). PDD quantifies changes in PDPs through new drift metrics that are sensitive to shifts in the data stream while remaining computationally efficient. This approach is aligned with MLOps practices, emphasizing continuous model monitoring and adaptive retraining in dynamic environments. Experiments on synthetic and real-world datasets demonstrate that PDD outperforms existing methods by maintaining high predictive performance while effectively balancing sensitivity and stability in drift signals. The results highlight its suitability for real-time applications, and the paper concludes by discussing the method's advantages, limitations, and potential extensions to broader use cases.

翻译：预测模型常因数据分布演变而导致性能衰退，这一现象称为数据漂移。其中，当解释变量与响应变量间的关系发生变化时形成的概念漂移，其检测与适应尤为困难。传统漂移检测方法通常依赖准确率或边际变量分布等指标，可能无法捕捉细微但重要的概念性变化。本文提出一种名为剖面漂移检测（PDD）的新方法，通过利用可解释人工智能工具——部分依赖剖面（PDPs），既能检测概念漂移，又能增强对其根因的理解。PDD通过新型漂移度量指标量化PDPs的变化，这些指标既能敏感捕捉数据流中的偏移，又保持计算高效性。该方法与MLOps实践相契合，强调在动态环境中进行持续模型监控与自适应重训练。在合成数据集与真实数据集上的实验表明，PDD通过有效平衡漂移信号的敏感度与稳定性，在保持高预测性能的同时优于现有方法。实验结果凸显了该方法在实时应用中的适用性，论文最后讨论了该方法的优势、局限及向更广泛用例扩展的可能性。