<ul><li>task.desc [<code><a href="/link/TaskDesc?package=mlr&version=2.0" rd-options="" data-mini-rdoc="mlr::TaskDesc">TaskDesc</a></code>]</li></ul>{Task description.}
  <item>method [<code>character</code>]</item>{Filter method.}
  <item>data [<code>data.frame</code>]</item>{Has columns: <code>name</code> = Names of features;
    <code>val</code> = Feature importance values; <code>type</code> = Feature column type.}

FilterValues

Interface to a large number of classification and regression
    techniques, including machine-readable parameter descriptions. There is
    also a n experimental extension for surival analysis and cost-sensitive
    learning. Generic resampling, including cross-validation, bootstrapping and
    subsampling. Hyperparameter tuning with modern optimization techniques.
    Filter and wrapper methods for feature selection. Extension of basic
    learners with additional operations common in machine learning.

Bernd Bischl

mlr: Machine Learning in R.

FilterValues function

Result of <code><a href="/link/getFilterValues?package=mlr&version=2.0" rd-options="" data-mini-rdoc="mlr::getFilterValues">getFilterValues</a></code>.

FilterValues: Result of `getFilterValues`.

Description

Arguments

See Also