varImpACC
Computes the variable importance regarding the accuracy (ACC).
varImpACC( object, mincriterion = 0, conditional = FALSE, threshold = 0.2, nperm = 1, OOB = TRUE, pre1.0_0 = conditional )
object |
An object as returned by cforest. |
mincriterion |
The value of the test statistic or 1 - p-value that must be exceeded in order to include a split in the computation of the importance. The default mincriterion = 0 guarantees that all splits are included. |
conditional |
The value of the test statistic or 1 - p-value that must be exceeded in order to include a split in the computation of the importance. The default mincriterion = 0 guarantees that all splits are included. |
threshold |
The threshold value for (1 - p-value) of the association between the variable of interest and a covariate, which must be exceeded inorder to include the covariate in the conditioning scheme for the variable of interest (only relevant if conditional = TRUE). A threshold value of zero includes all covariates. |
nperm |
The number of permutations performed. |
OOB |
A logical determining whether the importance is computed from the out-of-bag sample or the learning sample (not suggested). |
pre1.0_0 |
Prior to party version 1.0-0, the actual data values were permuted according to the original permutation importance suggested by Breiman (2001). Now the assignments to child nodes of splits in the variable of interest are permuted as described by Hapfelmeier et al. (2012), which allows for missing values in the explanatory variables and is more efficient wrt memory consumption and computing time. This method does not apply to conditional variable importances. |
Vector with computed permutation importance for each variable
data(iris) iris2 = iris iris2$Species = factor(iris$Species == "versicolor") iris.cf = cforest(Species ~ ., data = iris2,control = cforest_unbiased(mtry = 2, ntree = 50)) set.seed(123) a = varImpACC(object = iris.cf)
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