  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.
