This function trains the a machine learning model on the training data, using a num.folds-fold internal cross-validation scheme to find the optimal hyper-parameters of the model.

plm.trainer(feat, label, cl = "classif.cvglmnet", data.split = NULL,
  stratify = TRUE, modsel.crit = "auc", min.nonzero.coeff = 1)

Arguments

feat

features object

label

label object

cl

class of learner, directly passed to makeLearner

data.split

filename containing the training samples or list of training instances produced by data.splitter(), defaults to NULL leading to training on the complete dataset

stratify

boolean, should the folds in the internal cross-validation be stratified?

min.nonzero.coeff

integer number of minimum nonzero coefficients that should be present in the model

Value

an object of class makeWrappedModel