Observe how the performance changes with an increasing number of observations.
generateLearningCurveData( learners, task, resampling = NULL, percs = seq(0.1, 1, by = 0.1), measures, stratify = FALSE, show.info = getMlrOption("show.info") )
| learners | [(list of) Learner) |
|---|---|
| task | (Task) |
| resampling | (ResampleDesc | ResampleInstance) |
| percs | (numeric) |
| measures | [(list of) Measure) |
| stratify | ( |
| show.info | ( |
(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.
Other generate_plot_data:
generateCalibrationData(),
generateCritDifferencesData(),
generateFeatureImportanceData(),
generateFilterValuesData(),
generatePartialDependenceData(),
generateThreshVsPerfData(),
plotFilterValues()
Other learning_curve:
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)