Paper ID: 2305.00213

EBLIME: Enhanced Bayesian Local Interpretable Model-agnostic Explanations

Yuhao Zhong, Anirban Bhattacharya, Satish Bukkapatnam

We propose EBLIME to explain black-box machine learning models and obtain the distribution of feature importance using Bayesian ridge regression models. We provide mathematical expressions of the Bayesian framework and theoretical outcomes including the significance of ridge parameter. Case studies were conducted on benchmark datasets and a real-world industrial application of locating internal defects in manufactured products. Compared to the state-of-the-art methods, EBLIME yields more intuitive and accurate results, with better uncertainty quantification in terms of deriving the posterior distribution, credible intervals, and rankings of the feature importance.

Submitted: Apr 29, 2023