cv.default: Cross-validation

In targeted: Targeted Inference

Cross-validation

Description

Generic cross-validation function

Usage

## Default S3 method:
cv(
  object,
  data,
  response = NULL,
  nfolds = 5,
  rep = 1,
  weights = NULL,
  model.score = scoring,
  seed = NULL,
  shared = NULL,
  args.pred = NULL,
  args.future = list(),
  mc.cores,
  silent = FALSE,
  ...
)

Arguments

object	List of learner objects
data	data.frame or matrix
response	Response variable (vector or name of column in data).
nfolds	Number of folds (nfolds=0 simple test/train split into two folds 1:([n]/2), ([n]+1/2):n with last part used for testing)
rep	Number of repetitions (default 1)
weights	Optional frequency weights
model.score	Model scoring metric (default: MSE / Brier score). Must be a function with arguments response and prediction, and may optionally include weights, object and newdata arguments
seed	Random seed (argument parsed to future_Apply::future_lapply)
shared	Function applied to each fold with results send to each model
args.pred	Optional arguments to prediction function (see details below)
args.future	Arguments to future.apply::future_mapply
mc.cores	Optional number of cores. parallel::mcmapply used instead of future
silent	suppress all messages and progressbars
...	Additional arguments parsed to elements in object

Details

object should be list of objects of class learner. Alternatively, each element of models should be a list with a fitting function and a prediction function.

The response argument can optionally be a named list where the name is then used as the name of the response argument in models. Similarly, if data is a named list with a single data.frame/matrix then this name will be used as the name of the data/design matrix argument in models.

Value

An object of class 'cross_validated' is returned. See cross_validated-class for more details about this class and its generic functions.

Author(s)

Klaus K. Holst

See Also

cv.learner_sl

Examples

m <- list(learner_glm(Sepal.Length~1),
          learner_glm(Sepal.Length~Species),
          learner_glm(Sepal.Length~Species + Petal.Length))
x <- cv(m, rep=10, data=iris)
x

targeted documentation built on Jan. 12, 2026, 9:08 a.m.