  We propose Combined Selection and Uncertainty Visualizer (CSUV), which
estimates the set of true covariates in high-dimensional linear regression and
visualizes selection uncertainties by exploiting the (dis)agreement among
different base selectors. Our proposed method selects covariates that get
selected the most frequently by the different variable selection methods on
subsampled data. The method is generic and can be used with different existing
variable selection methods. We demonstrate its variable selection performance
using real and simulated data. The variable selection method and its
uncertainty illustration tool are publicly available as R package CSUV
(https://github.com/christineyuen/CSUV). The graphical tool is also available
online via https://csuv.shinyapps.io/csuv
