2019/09/13 by Wenjia Wang, Wang, Wenjia, Yi‐Hui Zhou +1
Computer Science · Mathematics · #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning and Data Classification #Methodology (stat.ME) #Statistical Methods and Inference
paper · pdf · doi:10.48550/arxiv.1909.06263
openalex publication_date 2019/09/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Modern statistical learning techniques have often emphasized prediction performance over interpretability, giving rise to "black box" models that may be difficult to understand, and to generalize to other settings. We conceptually divide a prediction model into interpretable and non-interpretable portions, as a means to produce models that are highly interpretable with little loss in performance. Implementation of the model is achieved by considering separability of the interpretable and non-interpretable portions, along with a doubly penalized procedure for model fitting. We specify conditions under which convergence of model estimation can be achieved via cyclic coordinate ascent, and the consistency of model estimation holds. We apply the methods to datasets for microbiome host trait prediction and a diabetes trait, and discuss practical tradeoff diagnostics to select models with high interpretability.