generateLearningCurveData(learners, task, resampling = NULL, percs = seq(0.1, 1, by = 0.1), measures, stratify = FALSE, show.info = getMlrOption("show.info"))Learner]
Learning algorithms which should be compared.Task]
The task.ResampleDesc | ResampleInstance]
Resampling strategy to evaluate the performance measure.
If no strategy is given a default "Holdout" will be performed.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.Measure]
Performance measures to generate learning curves for, representing the y-axis.logical(1)]
Only for classification:
Should the downsampled data be stratified according to the target classes?logical(1)]
Print verbose output on console?
Default is set via configureMlr.LearningCurveData]. A list containing:
Task]
The task.Measure]
Performance measures.data.frame] with columns:
learner Names of learners.
percentage Percentages drawn from the training split.
Measure passed to generateLearningCurveData.
generateCalibrationData,
generateCritDifferencesData,
generateFilterValuesData,
generatePartialPredictionData,
generateThreshVsPerfData,
getFilterValuesOther 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)
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