dropFeatures

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.

Patrick Schratz

Machine Learning in R

Bernd Bischl

Michel Lang

Lars Kotthoff

Julia Schiffner

Jakob Richter

Zachary Jones

Giuseppe Casalicchio

Mason Gallo

Jakob Bossek

Erich Studerus

Leonard Judt

Tobias Kuehn

Pascal Kerschke

Florian Fendt

Philipp Probst

Xudong Sun

Janek Thomas

Bruno Vieira

Laura Beggel

Quay Au

Martin Binder

Florian Pfisterer

Stefan Coors

Steve Bronder

Alexander Engelhardt

Christoph Molnar

Annette Spooner

dropFeatures function

<dl><dt>task</dt>
<dd>(Task) 
The task.</dd>
<dt>features</dt>
<dd>(<a href="/link/character?package=mlr&version=2.19.1" data-mini-rdoc="mlr::character">character</a>) 
Features to drop.</dd></dl>

Arguments

Drop some features of task. — dropFeatures

<dl>

<dt>task</dt>
<dd>(Task) 
The task.</dd>


<dt>features</dt>
<dd>(<a href='https://rdrr.io/r/base/character.html'>character</a>) 
Features to drop.</dd>

</dl>

Drop some features of task.

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dropFeatures: Drop some features of task.

Description

Usage

Value

Arguments

See Also