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stat.ML2023
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 exp…
stat.ML2018★ 8 cited
Balanced Random Survival Forests for Extremely Unbalanced, Right Censored Data
Kahkashan Afrin, Gurudev Illangovan, Sanjay S. Srivatsa +1
Accuracies of survival models for life expectancy prediction as well as critical-care applications are significantly compromised due to the sparsity of samples and extreme imbalanc…