makeWrappedModel

WrappedModel

[<code><a rd-options="" href="/link/Learner?package=mlr&version=2.10" data-mini-rdoc="mlr::Learner">Learner</a></code> | <code>character(1)</code>]
The learner.
If you pass a string the learner will be created via <code><a rd-options="" href="/link/makeLearner?package=mlr&version=2.10" data-mini-rdoc="mlr::makeLearner">makeLearner</a></code>.

learner

learner.model

[<code><a rd-options="" href="/link/TaskDesc?package=mlr&version=2.10" data-mini-rdoc="mlr::TaskDesc">TaskDesc</a></code>]
Task description object.

task.desc

[<code>integer</code>]
Subset used for training.

subset

[<code>character</code>]
Features used for training.

features

[named <code>list</code> of <code>character</code>]
Levels of factor variables (features and potentially target) in training data.
Named by variable name, non-factors do not occur in the list.

factor.levels

[<code>numeric(1)</code>]
Computation time for model fit in seconds.

time

Result from <code><a rd-options="" href="/link/train?package=mlr&version=2.10" 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.

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.

Bernd Bischl

Machine Learning in R

makeWrappedModel function

[<code><a rd-options='' href='Learner'>Learner</a></code> | <code>character(1)</code>]
The learner.
If you pass a string the learner will be created via <code><a rd-options='' href='makeLearner'>makeLearner</a></code>.

[<code><a rd-options='' href='TaskDesc'>TaskDesc</a></code>]
Task description object.

makeWrappedModel: Induced model of learner.

Description

Usage

Arguments

Value