Creates a wrapper, which can be used like any other learner object.
Models 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>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>

makeCostSensRegrWrapper

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.

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

Description

Usage

Arguments

Value

See Also