LearnerProperties

getLearnerProperties

hasLearnerProperties

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

learner

[<code>character</code>]
Vector of properties to query.

props

Properties can be accessed with <code>getLearnerProperties(learner)</code>, which returns a
character vector.The learner properties are defined as follows:
<dl>
 
 
 
 
 
 
 
 
</dl>

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

LearnerProperties 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>.

Properties can be accessed with <code>getLearnerProperties(learner)</code>, which returns a
character vector.

The learner properties are defined as follows:
<dl>
 
 
 
 
 
 
 
 
</dl>

LearnerProperties: Query properties of learners.

Description

Usage

Arguments

Value

See Also