getLearnerModel

[<code><a rd-options="" href="/link/WrappedModel?package=mlr&version=2.9" data-mini-rdoc="mlr::WrappedModel">WrappedModel</a></code>]
The model, returned by e.g., <code><a rd-options="" href="/link/train?package=mlr&version=2.9" data-mini-rdoc="mlr::train">train</a></code>.

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 <code><a rd-options="" href="/link/WrappedModel?package=mlr&version=2.9" data-mini-rdoc="mlr::WrappedModel">WrappedModel</a></code>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.

Bernd Bischl

Machine Learning in R

getLearnerModel function

[<code><a rd-options='' href='WrappedModel'>WrappedModel</a></code>]
The model, returned by e.g., <code><a rd-options='' href='train'>train</a></code>.

[<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 <code><a rd-options='' href='WrappedModel'>WrappedModel</a></code>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>.

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

Description

Usage

Arguments

Value