let's say I'm creating such learning curve (possible little errors in code, it's just a sample). What I want is rather a classical learning curve, where you make enlarge the training set keeping the validation/test set the same size.
learningCurve <- generateLearningCurveData("regr.glmnet",
makeResampleDesc(method = "cv", iters = 5, predict = "both"),
seq(0.1, 1, by = 0.1),
list(setAggregation(auc, train.mean), setAggregation(auc, test.mean))
Note that when we train on a small subset of the training data, the training error is computed using this subset, not the full training set.
As a reference for future readers, this will be fixed and here's the github issue