analyzeFeatSelResult

[<code><a rd-options="" href="/link/FeatSelResult?package=mlr&version=2.10" data-mini-rdoc="mlr::FeatSelResult">FeatSelResult</a></code>]
The result of of <code><a rd-options="" href="/link/selectFeatures?package=mlr&version=2.10" data-mini-rdoc="mlr::selectFeatures">selectFeatures</a></code>.

[<code>logical(1)</code>]
Per iteration: Print only the selected feature (or all features that were evaluated)?
Default is <code>TRUE</code>.

reduce

This function prints the steps <code><a rd-options="" href="/link/selectFeatures?package=mlr&version=2.10" 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. Currently only implemented for sequential feature selection.

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.

Bernd Bischl

Machine Learning in R

analyzeFeatSelResult function

[<code><a rd-options='' href='FeatSelResult'>FeatSelResult</a></code>]
The result of of <code><a rd-options='' href='selectFeatures'>selectFeatures</a></code>.

analyzeFeatSelResult: Show and visualize the steps of feature selection.

Description

Usage

Arguments

Value

See Also