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].

### See Also

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

Other learning_curve: `plotLearningCurve`

### 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)

[Package mlr version 2.12.1 Index]