Creates a wrapper, which can be used like any other learner object.
The classification model can easily be accessed via <code><a href="/link/getHomogeneousEnsembleModels?package=mlr&version=2.3" rd-options="" data-mini-rdoc="mlr::getHomogeneousEnsembleModels">getHomogeneousEnsembleModels</a></code>.<p></p><p>This is a very naive learner, where the costs are transformed into classification labels -
the label for each case is the name of class with minimal costs.
(If ties occur, the label which is better on average w.r.t. costs over all training data is
preferred.)
Then the classifier is fitted to that data and subsequently used for prediction.</p>

makeCostSensClassifWrapper

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.

makeCostSensClassifWrapper: Wraps a classification learner for use in cost-sensitive learning.

Description

Usage

Arguments

Value

See Also