makeExtractFDAFeatMethod

(<code>function(data, target, col, ...)</code>)
Function to learn and extract information on functional column <code>col</code>.
Arguments are:<ul>
<li>data <a rd-options="" href="/link/data.frame?package=mlr&version=2.15.0" data-mini-rdoc="mlr::data.frame">data.frame</a>
Data.frame with one row per observation of a single functional feature
or time series and one column per measurement time point.
All entries need to be numeric.</li>
<li>data <a rd-options="" href="/link/data.frame?package=mlr&version=2.15.0" data-mini-rdoc="mlr::data.frame">data.frame</a>
Data.frame containing matricies with one row per observation of a single functional
or time series and one column per measurement time point. All entries need to be numeric.</li>
<li>target <a rd-options="" href="/link/character?package=mlr&version=2.15.0" data-mini-rdoc="mlr::character">character</a>
Name of the target variable. Default: &#8220;NULL&#8221;.
The variable is only set to be consistent with the API.</li>
<li>col (<a rd-options="" href="/link/character?package=mlr&version=2.15.0" data-mini-rdoc="mlr::character">character</a> | <a rd-options="" href="/link/numeric?package=mlr&version=2.15.0" data-mini-rdoc="mlr::numeric">numeric</a>)
column names or indices, the extraction should be performed on.
The function has to return a named list of values.</li>
</ul>

learn

(<code>function(data, target, col, ...)</code>)
Function used for reextracting data in predict phase.
Can be equal to <code>learn</code>.

reextract

(<a rd-options="" href="/link/list?package=mlr&version=2.15.0" data-mini-rdoc="mlr::list">list</a>)
Named list of arguments to pass to <code>learn</code> via <code>...</code>.

args

This can be used to implement custom feature FDA extraction.

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

makeExtractFDAFeatMethod function

(<code>function(data, target, col, ...)</code>)
Function to learn and extract information on functional column <code>col</code>.
Arguments are:<ul>
<li>data <a rd-options='' href='data.frame'>data.frame</a>
Data.frame with one row per observation of a single functional feature
or time series and one column per measurement time point.
All entries need to be numeric.</li>
<li>data <a rd-options='' href='data.frame'>data.frame</a>
Data.frame containing matricies with one row per observation of a single functional
or time series and one column per measurement time point. All entries need to be numeric.</li>
<li>target <a rd-options='' href='character'>character</a>
Name of the target variable. Default: &#8220;NULL&#8221;.
The variable is only set to be consistent with the API.</li>
<li>col (<a rd-options='' href='character'>character</a> | <a rd-options='' href='numeric'>numeric</a>)
column names or indices, the extraction should be performed on.
The function has to return a named list of values.</li>
</ul>

(<a rd-options='' href='list'>list</a>)
Named list of arguments to pass to <code>learn</code> via <code>...</code>.

Constructor for FDA feature extraction methods. — makeExtractFDAFeatMethod

Constructor for FDA feature extraction methods.

makeExtractFDAFeatMethod: Constructor for FDA feature extraction methods.

Description

Usage

Arguments

See Also