A unified framework for global and local interpretability using adaptive derivative-ordered random explanation
The interpretability of complex machine learning models is of paramount importance, especially in real-world high-stakes domains such as healthcare and finance. However, existing post-hoc interpretability methods suffer from inherent limitations: fragmented analytical processes, inadequate capacity to model nonlinear feature interactions, computational inefficiencies, and over-reliance on specific model architectures. To address these challenges, this paper provides a novel method - Adaptive Derivative-Ordered Random Explanation (ADORE) - that leverages first- and second-order derivatives to accommodate nonlinear model complexities, while enabling effective capture of feature-sample inter...
| Authors | Lemen Chao, Ming Lei, Anran Fang |
|---|---|
| Primary category | cs.LG |
| Published | 15 September 2026 |
| Last updated at source | 16 September 2026 |
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