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:

- 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.

### See Also

Other generate_plot_data: `generateCalibrationData()`, `generateCritDifferencesData()`, `generateFeatureImportanceData()`, `generateFilterValuesData()`, `generatePartialDependenceData()`, `generateThreshVsPerfData()`, `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 2.17.1 Index]