Description Usage Arguments Details Value Examples
Heuristic on variable importance, taken as averages from the variable importances calculated for each tree.
1 | varimp_stratified_rf(model, metric = "usage", agg_type = "simple")
|
model |
A stratified_rf model. |
metric |
How to calculate the variable importance from each tree. Either "usage" or "splits". |
agg_type |
How to aggregate the variable importances obtained from each tree. Either "simple" for a simple average, or "weighted" for an average weighted by each tree's accuracy. |
Methods are taken directly from the C5.0 trees. Currently doesn't support permutation tests.
A named data frame with the importance score of each variable, sorted from largest to smallest.
1 2 3 4 5 | data(iris)
groups <- list(c("Sepal.Length","Sepal.Width"),c("Petal.Length","Petal.Width"))
mtry <- c(1,1)
m <- stratified_rf(iris,"Species",groups,mtry,ntrees=2,multicore=FALSE)
varimp_stratified_rf(m)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.