| nrcv_rusranger | R Documentation |
Run a grid search in a nested cross validation.
nrcv_rusranger( x, y, searchspace, nouterfolds = 5, ninnerfolds = 5, nrepcv = 2, ... )
x |
|
y |
|
searchspace |
|
nouterfolds |
|
ninnerfolds |
|
nrepcv |
|
... |
further arguments passed to |
list, with an element per nouterfolds containing the following
subelements:
model selected ranger model.
indextrain index of the used training items.
indextest index of the used test items.
prediction predictions results.
truth original labels/classes.
performance resulting performance (AUC).
selectedparams select hyperparameters.
gridsearch data.frame, results of the grid search.
nouterfolds integer(1).
ninnerfolds integer(1).
nrepcv integer(1).
The reported performance could slightly differ from the median performance
in the reported gridsearch. After the gridsearch rusranger is trained again
with the best hyperparameters which results in a new subsampling.
set.seed(20220324)
iris <- subset(iris, Species != "setosa")
searchspace <- expand.grid(
mtry = c(2, 3),
num.trees = c(500, 1000)
)
## n(outer|inner) folds and nrepcv are too low for real world applications,
## and are just used for demonstration and to keep the run time of the examples
## low
nrcv_rusranger(
iris[-5], as.numeric(iris$Species == "versicolor"),
searchspace = searchspace, nouterfolds = 3, ninnerfolds = 3, nrepcv = 1
)
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