Creates a wrapper, which can be used like any other learner object.
Models can easily be accessed via <code><a href="/link/getCostSensRegrModels?package=mlr&version=2.0" rd-options="" data-mini-rdoc="mlr::getCostSensRegrModels">getCostSensRegrModels</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 a n experimental extension for surival analysis and cost-sensitive
    learning. Generic resampling, including cross-validation, bootstrapping and
    subsampling. Hyperparameter tuning with modern optimization techniques.
    Filter and wrapper methods for feature selection. Extension of basic
    learners with additional operations common in machine learning.

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

Description

Usage

Arguments

Value