emil (version 2.2.3)

learning_curve: Learning curve analysis

Description

This function studies the change in permformance as the sizes of the training set is varied. In case the studied modeling procedures cannot produce models on the smallest training sets, please use .return_error=TRUE (see evaluate.

Usage

learning_curve(procedure, x, y, test_fraction, nfold = 100, ...,
  .verbose = TRUE)

References

Richard O Duda, Peter E Hart, and David G Stork. Pattern Classification. Wiley, 2nd edition, 2000. ISBN 978-0-471-05669-0.

Examples

options(emil_max_indent=3)
lc <- learning_curve(c(Linear="lda", Quadratic="qda"),
                     iris[-5], iris$Species, test_fraction=7:3/10)
plot(lc)