generateLearningCurveData: Generates a learning curve.
In riebetob/mlr: Machine Learning in R

Description Usage Arguments Value See Also Examples

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

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

`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`.

[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.

Other generate_plot_data: generateCalibrationData, generateCritDifferencesData, generateFeatureImportanceData, generateFilterValuesData, generateFunctionalANOVAData, generatePartialDependenceData, generateThreshVsPerfData, getFilterValues, plotFilterValues

Other learning_curve: plotLearningCurveGGVIS, plotLearningCurve

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)