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)
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 |
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 |
an object of class makeWrappedModel