mlr (version 2.18.0)

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.

See Also

Other generate_plot_data: generateCalibrationData(), generateCritDifferencesData(), generateFeatureImportanceData(), generateFilterValuesData(), generatePartialDependenceData(), generateThreshVsPerfData(), plotFilterValues()

Other learning_curve: plotLearningCurve()

Examples

# NOT RUN {
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)
# }

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