fastvarImp.RdParallelized version of varImp function from varImp package, which computes the variable importance for arbitrary measures from the measures package.
fastvarImp(object, mincriterion = 0, conditional = FALSE,
threshold = 0.2, nperm = 1, OOB = TRUE,
pre1.0_0 = conditional, measure = "multiclass.Brier",
parallel = TRUE, ...)An object as returned by cforest (or fastcforest).
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.
a logical determining whether unconditional or conditional computation of the importance is performed.
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.
The number of permutations performed.
A logical determining whether the importance is computed from the out-of-bag sample or the learning sample (not suggested).
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.
The name of the measure of the measures package that should be used for the variable importance calculation.
Logical indicating whether or not to run fastvarImp in parallel using a backend provided by the foreach package. Default is FALSE.
Further arguments (like positive or negative class) that are needed by the measure.
The code is adapted from varImp function in varImp package.
Vector with computed permutation importance for each variable.
varImp, fastvarImpAUC, cforest, fastcforest
data(iris)
iris2 = iris
iris2$Species = factor(iris$Species == "versicolor")
iris.cf = party::cforest(Species ~ ., data = iris2,
control = party::cforest_unbiased(mtry = 2, ntree = 50))
fastvarImp(object = iris.cf, measure='ACC', parallel=FALSE)
#> Sepal.Length Sepal.Width Petal.Length Petal.Width
#> 0.01600000 0.08036364 0.16400000 0.18472727