Result from <code><a href="/link/train?package=mlr&version=1.1-18" rd-options="" data-mini-rdoc="mlr::train">train</a></code>. It internally stores the
  underlying fitted model, the subset used for training,
  features used for training and computtation time for
  training.

makeWrappedModel

Interface to a large number of classification and
        regression techniques, including machine-readable parameter
        descriptions. Generic resampling, including cross-validation,
        bootstrapping and subsampling. Hyperparameter tuning with
        modern optimization techniques. Filter and wrapper methods for
        feature selection. Extension of basic learners with additional
        operations. Nested resampling.

makeWrappedModel: Induced model of learner.

Description

Usage

Arguments

Value

Details