mlr: generateLearningCurveData – R documentation

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mlr

generateLearningCurveData

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:

The Task
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)

mlr

Machine Learning in R

v2.19.0

BSD_2_clause + file LICENSE

Authors

Bernd Bischl [aut] (<https://orcid.org/0000-0001-6002-6980>), Michel Lang [aut] (<https://orcid.org/0000-0001-9754-0393>), Lars Kotthoff [aut], Patrick Schratz [aut, cre] (<https://orcid.org/0000-0003-0748-6624>), Julia Schiffner [aut], Jakob Richter [aut], Zachary Jones [aut], Giuseppe Casalicchio [aut] (<https://orcid.org/0000-0001-5324-5966>), Mason Gallo [aut], Jakob Bossek [ctb] (<https://orcid.org/0000-0002-4121-4668>), Erich Studerus [ctb] (<https://orcid.org/0000-0003-4233-0182>), Leonard Judt [ctb], Tobias Kuehn [ctb], Pascal Kerschke [ctb] (<https://orcid.org/0000-0003-2862-1418>), Florian Fendt [ctb], Philipp Probst [ctb] (<https://orcid.org/0000-0001-8402-6790>), Xudong Sun [ctb] (<https://orcid.org/0000-0003-3269-2307>), Janek Thomas [ctb] (<https://orcid.org/0000-0003-4511-6245>), Bruno Vieira [ctb], Laura Beggel [ctb] (<https://orcid.org/0000-0002-8872-8535>), Quay Au [ctb] (<https://orcid.org/0000-0002-5252-8902>), Martin Binder [ctb], Florian Pfisterer [ctb], Stefan Coors [ctb], Steve Bronder [ctb], Alexander Engelhardt [ctb], Christoph Molnar [ctb], Annette Spooner [ctb]

Initial release