This function prints the steps <code><a href="/link/selectFeatures?package=mlr&version=2.3" rd-options="" data-mini-rdoc="mlr::selectFeatures">selectFeatures</a></code> took to find its optimal set
of features and the reason why it stopped.
It can also print information about all calculations done in each intermediate step.<p></p><p>Currently only implemented for sequential feature selection.</p>

analyzeFeatSelResult

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.

analyzeFeatSelResult: Show and visualize the steps of feature selection.

Description

Usage

Arguments

Value

See Also