Beyond Linear Surrogates: High-Fidelity Local Explanations for Black-Box Models

ArXi:2512.05556v3 Announce Type: replace-cross With the increasing complexity of black-box machine learning models and their adoption in high-stakes areas, it is critical to provide explanations for their predictions. Existing local explanation methods lack in generating high-fidelity explanations. This paper proposes a novel local model agnostic explanation method to generate high-fidelity explanations using multivariate adaptive regression splines (MARS) and N-ball sampling strategies.