vimp - Perform Inference on Algorithm-Agnostic Variable Importance
Calculate point estimates of and valid confidence intervals for nonparametric, algorithm-agnostic variable importance measures in high and low dimensions, using flexible estimators of the underlying regression functions. For more information about the methods, please see Williamson et al. (Biometrics, 2020), Williamson et al. (JASA, 2021), and Williamson and Feng (ICML, 2020).
Last updated 7 days ago
machine-learningnonparametric-statisticsstatistical-inferencevariable-importance
6.77 score 22 stars 67 scripts 462 downloadsflevr - Flexible, Ensemble-Based Variable Selection with Potentially Missing Data
Perform variable selection in settings with possibly missing data based on extrinsic (algorithm-specific) and intrinsic (population-level) variable importance. Uses a Super Learner ensemble to estimate the underlying prediction functions that give rise to estimates of variable importance. For more information about the methods, please see Williamson and Huang (2023+) <arXiv:2202.12989>.
Last updated 1 years ago
4.88 score 5 stars 2 scripts 178 downloads