Optimizes the features for a classification or regression problem by choosing a variable selection wrapper approach.
Allows for different optimization methods, such as forward search or a genetic algorithm.
You can select such an algorithm (and its settings)
by passing a corresponding control object. For a complete list of implemented algorithms look at the
subclasses of [<code><a href="/link/FeatSelControl?package=mlr&version=2.7" rd-options="" data-mini-rdoc="mlr::FeatSelControl">FeatSelControl</a></code>].<p></p><p>All algorithms operate on a 0-1-bit encoding of candidate solutions. Per default a single bit corresponds
to a single feature, but you are able to change this by using the arguments <code>bit.names</code>
and <code>bits.to.features</code>. Thus allowing you to switch on whole groups of features with a single bit.</p>

selectFeatures

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.

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selectFeatures: Feature selection by wrapper approach.

Description

Usage

Arguments

Value

See Also

Examples