makeFilterEnsemble

(<code>character(1)</code>)
Identifier for the filter.

name

the base filter methods which the ensemble method
will use.

base.methods

(<code>character(1)</code>)
Short description of the filter.

desc

(<code>function(task, nselect, ...</code>)
Function which takes a task and returns a named numeric vector of scores,
one score for each feature of <code>task</code>.
Higher scores mean higher importance of the feature.
At least <code>nselect</code> features must be calculated, the remaining may be
set to <code>NA</code> or omitted, and thus will not be selected.
the original order will be restored if necessary.

Creates and registers custom ensemble feature filters. Implemented ensemble filters
can be listed with listFilterEnsembleMethods. Additional
documentation for the <code>fun</code> parameter specific to each filter can
be found in the description.

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

makeFilterEnsemble function

<dl><dt>name</dt>
<dd>(<code>character(1)</code>) 
Identifier for the filter.</dd>
<dt>base.methods</dt>
<dd>the base filter methods which the ensemble method
will use.</dd>
<dt>desc</dt>
<dd>(<code>character(1)</code>) 
Short description of the filter.</dd>
<dt>fun</dt>
<dd>(<code>function(task, nselect, ...</code>) 
Function which takes a task and returns a named numeric vector of scores,
one score for each feature of <code>task</code>.
Higher scores mean higher importance of the feature.
At least <code>nselect</code> features must be calculated, the remaining may be
set to <code>NA</code> or omitted, and thus will not be selected.
the original order will be restored if necessary.</dd></dl>

Arguments

Create an ensemble feature filter. — makeFilterEnsemble

<dl>

<dt>name</dt>
<dd>(<code>character(1)</code>) 
Identifier for the filter.</dd>


<dt>base.methods</dt>
<dd>the base filter methods which the ensemble method
will use.</dd>


<dt>desc</dt>
<dd>(<code>character(1)</code>) 
Short description of the filter.</dd>


<dt>fun</dt>
<dd>(<code>function(task, nselect, ...</code>) 
Function which takes a task and returns a named numeric vector of scores,
one score for each feature of <code>task</code>.
Higher scores mean higher importance of the feature.
At least <code>nselect</code> features must be calculated, the remaining may be
set to <code>NA</code> or omitted, and thus will not be selected.
the original order will be restored if necessary.</dd>

</dl>

Create an ensemble feature filter.

makeFilterEnsemble: Create an ensemble feature filter.

Description

Usage

Value

Arguments

See Also