Bayesian subset selection and variable importance for interpretable prediction and classification.

ABSTRACT: Subset selection is a valuable tool for interpretable learning, scientific discovery, and data compression. However, classical subset selection is often avoided due to selection instability, lack of regularization, and difficulties with post-selection inference. We address these challenges from a Bayesian perspective. Given any Bayesian predictive model ℳ, we extract a family of near-optimal subsets of variables for linear prediction or classification. This strategy deemphasizes the role of a single "best" subset and instead advances the broader perspective that often many subsets are highly competitive. The acceptable family of subsets offers a new pathway for model interpretation and is neatly summarized by key members such as the smallest acceptable subset, along with new

SUBMITTER: Kowal DR 

PROVIDER: S-EPMC10723825 | biostudies-literature | 2022 Jan-Dec

REPOSITORIES: biostudies-literature