mlr (version 2.17.0)

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:

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(), 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)
# }