CostSensRegrModel

CostSensRegrWrapper

makeCostSensRegrWrapper

[<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 regression 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


Creates a wrapper, which can be used like any other learner object.
Models can easily be accessed via <code><a rd-options="" href="/link/getLearnerModel?package=mlr&version=2.9" data-mini-rdoc="mlr::getLearnerModel">getLearnerModel</a></code>.<p></p><p>For each class in the task, an individual regression model is fitted for the costs of that class.
During prediction, the class with the lowest predicted costs is selected.
</p>

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

makeCostSensRegrWrapper function

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


Creates a wrapper, which can be used like any other learner object.
Models can easily be accessed via <code><a rd-options='' href='getLearnerModel'>getLearnerModel</a></code>.

For each class in the task, an individual regression model is fitted for the costs of that class.
During prediction, the class with the lowest predicted costs is selected.


makeCostSensRegrWrapper: Wraps a regression learner for use in cost-sensitive learning.

Description

Usage

Arguments

Value

See Also