A model-agnostic approach for explaining the predictions on clustered data
Document Type
Conference Proceeding
Publication Date
11-1-2019
Abstract
Machine learning models especially deep neural network models have shown great potential in making decisions when analyzing clustered or longitudinal data. However, lack of model transparency is a major concern in risk sensitive domains such as social science and medical diagnosis. Despite the early success of explaining machine learning models, there is a lack of explanation methods that can be applied to any predictors on clustered data since most of the existing models assume that all observations are independent of each other. In this paper, we address this deficiency and propose to use a linear mixed model to mimic the local behavior of any complex model on clustered data, which can also improve the fidelity of the explanation method to the complex models. We apply our method to explain several models including a deep neural network model on two tasks including movie recommendation and medical record diagnosis. Experiment results show that our model outperforms the baseline models on several metrics such as fidelity and exactness.
Publication Source (Journal or Book title)
Proceedings - IEEE International Conference on Data Mining, ICDM
First Page
1528
Last Page
1533
Recommended Citation
Zhou, Z., Sun, M., & Chen, J. (2019). A model-agnostic approach for explaining the predictions on clustered data. Proceedings - IEEE International Conference on Data Mining, ICDM, 2019-November, 1528-1533. https://doi.org/10.1109/ICDM.2019.00202