makePreprocWrapperCaret

[<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

[any]
See <code><a rd-options="caret" href="/link/preProcess?package=mlr&version=2.10&to=caret" data-mini-rdoc="caret::preProcess">preProcess</a></code> for parameters not listed above.
If you use them you might want to define them in the <code>add.par.set</code> so that they can be tuned.

Fuses a learner with preprocessing methods provided by <code><a rd-options="caret" href="/link/preProcess?package=mlr&version=2.10&to=caret" data-mini-rdoc="caret::preProcess">preProcess</a></code>.
 Before training the preprocessing will be performed and the preprocessing model will be stored.
 Before prediction the preprocessing model will transform the test data according to the trained model. After being wrapped the learner will support missing values although this will only be the case if <code>ppc.knnImpute</code>, <code>ppc.bagImpute</code> or <code>ppc.medianImpute</code> is set to <code>TRUE</code>.

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

makePreprocWrapperCaret 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>.

[any]
See <code><a rd-options='caret' href='preProcess'>preProcess</a></code> for parameters not listed above.
If you use them you might want to define them in the <code>add.par.set</code> so that they can be tuned.

makePreprocWrapperCaret: Fuse learner with preprocessing.

Description

Usage

Arguments

Value

See Also