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 f...
Le-Men Chao, Ming Lei, An-Ran Fang
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