Returns the class names of learning algorithms which have specific characteristics, e.g.
whether they supports missing values, case weights, etc.Note that the packages of all learners are loaded during the search.Note that for general cost-sensitive learning, mlr currently supports mainly
<dquote>wrapper</dquote> approaches like <code><a href="/link/CostSensWeightedPairsWrapper?package=mlr&version=2.1" rd-options="" data-mini-rdoc="mlr::CostSensWeightedPairsWrapper">CostSensWeightedPairsWrapper</a></code>,
which are not listed, as they are not basic R learning algorithms.

listLearners

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.

listLearners: Find matching learning algorithms.

Description

Usage

Arguments

Value