UniformLIME: A Uniformly Perturbed Local Interpretable Model-Agnostic Explanations Approach for Aerodynamics
Enshuo Jiang
Abstract
Abstract Machine learning and deep learning are widely used in the field of aerodynamics. But most models are often seen as black boxes due to lack of interpretability. Local Interpretable Model-agnostic Explanations (LIME) is a popular method that uses a local surrogate model to explain a single instance of machine learning. Its main disadvantages are the instability of the explanations and low local fidelity. In this paper, we propose an original modification to LIME by employing a new perturbed sample generation method for aerodynamic tabular data in regression model, which makes the differences between perturbed samples and the input instance vary in a larger range. We make several comparisons with three subtasks and show that our proposed method results in better metrics.