targeted (version 0.6)

cv.default: Cross-validation

Description

Generic cross-validation function

Usage

# S3 method for default
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,
  ...
)

Value

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

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

Author

Klaus K. Holst

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.

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