getLearnerModel

(<a rd-options="" href="/link/WrappedModel?package=mlr&version=2.19.0" data-mini-rdoc="mlr::WrappedModel">WrappedModel</a>)
The model, returned by e.g., <a rd-options="" href="/link/train?package=mlr&version=2.19.0" data-mini-rdoc="mlr::train">train</a>.

model

(<code>logical(1)</code>)
Some learners are not basic learners from R, but implemented in mlr as meta-techniques.
Examples are everything that inherits from <code>HomogeneousEnsemble</code>.
In these cases, the <code>learner.model</code> is often a list of mlr <a rd-options="" href="/link/WrappedModel?package=mlr&version=2.19.0" data-mini-rdoc="mlr::WrappedModel">WrappedModel</a>s.
This option allows to strip them further to basic R models.
The option is simply ignored for basic learner models.
Default is <code>FALSE</code>.

more.unwrap

Get underlying R model of learner integrated into mlr.

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.

Patrick Schratz

Machine Learning in R

Bernd Bischl

Michel Lang

Lars Kotthoff

Julia Schiffner

Jakob Richter

Zachary Jones

Giuseppe Casalicchio

Mason Gallo

Jakob Bossek

Erich Studerus

Leonard Judt

Tobias Kuehn

Pascal Kerschke

Florian Fendt

Philipp Probst

Xudong Sun

Janek Thomas

Bruno Vieira

Laura Beggel

Quay Au

Martin Binder

Florian Pfisterer

Stefan Coors

Steve Bronder

Alexander Engelhardt

Christoph Molnar

Annette Spooner

getLearnerModel function

<dl><dt>model</dt>
<dd>(WrappedModel) 
The model, returned by e.g., train.</dd>
<dt>more.unwrap</dt>
<dd>(<code>logical(1)</code>) 
Some learners are not basic learners from R, but implemented in mlr as meta-techniques.
Examples are everything that inherits from <code>HomogeneousEnsemble</code>.
In these cases, the <code>learner.model</code> is often a list of mlr WrappedModels.
This option allows to strip them further to basic R models.
The option is simply ignored for basic learner models.
Default is <code>FALSE</code>.</dd></dl>

Arguments

Get underlying R model of learner integrated into mlr. — getLearnerModel

<dl>

<dt>model</dt>
<dd>(WrappedModel) 
The model, returned by e.g., train.</dd>


<dt>more.unwrap</dt>
<dd>(<code>logical(1)</code>) 
Some learners are not basic learners from R, but implemented in mlr as meta-techniques.
Examples are everything that inherits from <code>HomogeneousEnsemble</code>.
In these cases, the <code>learner.model</code> is often a list of mlr WrappedModels.
This option allows to strip them further to basic R models.
The option is simply ignored for basic learner models.
Default is <code>FALSE</code>.</dd>

</dl>

Get underlying R model of learner integrated into mlr.

getLearnerModel: Get underlying R model of learner integrated into mlr.

Description

Usage

Value

Arguments