FilterValues

generateFilterValuesData

[<code><a rd-options="" href="/link/Task?package=mlr&version=2.9" data-mini-rdoc="mlr::Task">Task</a></code>]
The task.

task

[<code>character</code>]
Filter method(s), see above.
Default is &#147;rf.importance&#148;.

method

[<code>integer(1)</code>]
Number of scores to request. Scores are getting calculated for all features per default.

nselect

[any]
Passed down to selected method. Can only be use if <code>method</code> contains one element.

[named list]
Extra args passed down to filter methods. List elements are named with the filter
<code>method</code> name the args should be passed down to.
A more general and flexible option than <code>...</code>.
Default is empty list.

more.args


Calculates numerical filter values for features.
For a list of features, use <code><a rd-options="" href="/link/listFilterMethods?package=mlr&version=2.9" data-mini-rdoc="mlr::listFilterMethods">listFilterMethods</a></code>.


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

generateFilterValuesData function

[<code><a rd-options='' href='Task'>Task</a></code>]
The task.


Calculates numerical filter values for features.
For a list of features, use <code><a rd-options='' href='listFilterMethods'>listFilterMethods</a></code>.


generateFilterValuesData: Calculates feature filter values.

Description

Usage

Arguments

Value

See Also