learning_curve: Learning curve analysis

In emil: Evaluation of Modeling without Information Leakage

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)

Arguments

procedure	modeling_procedure.
x	Dataset descriptors.
y	Response.
test_fraction	Fraction of dataset to hold out, i.e. use as test set. Defaults 20 logarithmically distributed values ranging from all but 5 observations per class in the largest test set to only 5 observations per class in the smallest test set.
nfold	How many holdout folds that should be calculated.
...	Sent to evaluate.
.verbose	Whether to print an activity log. Set to -1 to also suppress output generated from the procedure's functions.

Author(s)

Christofer Bäcklin

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=c(.7, .5, .3))
plot(lc)

emil documentation built on Aug. 1, 2018, 1:03 a.m.