R: Generates a learning curve.

generateLearningCurveData {mlr}

R Documentation

Generates a learning curve.

Description

Observe how the performance changes with an increasing number of observations.

Usage

generateLearningCurveData(learners, task, resampling = NULL,
  percs = seq(0.1, 1, by = 0.1), measures, stratify = FALSE,
  show.info = getMlrOption("show.info"))

Arguments

`learners`	[(list of) [Learner]) Learning algorithms which should be compared.
`task`	(Task) The task.
`resampling`	([ResampleDesc] \| [ResampleInstance]) Resampling strategy to evaluate the performance measure. If no strategy is given a default "Holdout" will be performed.
`percs`	([numeric]) Vector of percentages to be drawn from the training split. These values represent the x-axis. Internally [makeDownsampleWrapper] is used in combination with [benchmark]. Thus for each percentage a different set of observations is drawn resulting in noisy performance measures as the quality of the sample can differ.
`measures`	[(list of) [Measure]) Performance measures to generate learning curves for, representing the y-axis.
`stratify`	('logical(1)') Only for classification: Should the downsampled data be stratified according to the target classes?
`show.info`	(`logical(1)`) Print verbose output on console? Default is set via configureMlr.

Value

([LearningCurveData]). A 'list' containing:

task

[[Task])
The task.

measures

[(list of) [Measure])
Performance measures.

data

([data.frame]) with columns:

'learner' Names of learners.
'percentage' Percentages drawn from the training split.
One column for each [Measure] passed to [generateLearningCurveData].

Examples

r = generateLearningCurveData(list("classif.rpart", "classif.knn"),
  task = sonar.task, percs = seq(0.2, 1, by = 0.2),
  measures = list(tp, fp, tn, fn), resampling = makeResampleDesc(method = "Subsample", iters = 5),
  show.info = FALSE)
plotLearningCurve(r)