no code implementations • 15 Apr 2022 • Wenying Deng, Beau Coker, Rajarshi Mukherjee, Jeremiah Zhe Liu, Brent A. Coull
We develop a simple and unified framework for nonlinear variable selection that incorporates uncertainty in the prediction function and is compatible with a wide range of machine learning models (e. g., tree ensembles, kernel methods, neural networks, etc).