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Table 1 Summary of some characteristics of VIMs

From: Intervention in prediction measure: a new approach to assessing variable importance for random forests

Methods GVIM PVIM (CART-RF) PVIM (CIT-RF) CPVIM varSelRF varSelMD IPM
Main references [43] [2] [13, 15] [25] [26] [10, 33] [11] and this manuscript
Key characteristic Node impurity Accuracy after variable permutation Accuracy after variable permutation Alternative of PVIM; Conditional permutation Backward elimination Variable selection based on MD Variables intervening in prediction
RF-based CART-RF CART-RF CIT-RF CIT-RF CART-RF CART-RF CART-RF or CIT-RF
Handling of response Univariate Univariate Univariate Univariate Categorical All (multivariate included) All (multivariate included)
Main R implementation randomForest [18, 19] randomForest [18, 19] randomForestSRC [2022] party [2325] party [2325] varSelRF [27, 28] randomForestSRC [2022] Additional file 2
Casewise importance No Yes Not defined Not defined No No Yes