Result from <code><a href="/link/train?package=mlr&version=2.3" 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, levels of factors in the
data set and computation time that was spent for training.Object members: See arguments.The constructor <code>makeWrappedModel</code> is mainly for internal use.

makeWrappedModel

Interface to a large number of classification and regression
    techniques, including machine-readable parameter descriptions. There is
    also an experimental extension for survival analysis, clustering and
    general, example-specific cost-sensitive learning. Generic resampling,
    including cross-validation, bootstrapping and subsampling. Hyperparameter
    tuning with modern optimization techniques, for single- and multi-objective
    problems. Filter and wrapper methods for feature selection. Extension of
    basic learners with additional operations common in machine learning, also
    allowing for easy nested resampling. Most operations can be parallelized.

makeWrappedModel: Induced model of learner.

Description

Usage

Arguments

Value