First, calls <code><a href="/link/getFilterValues?package=mlr&version=2.0" rd-options="" data-mini-rdoc="mlr::getFilterValues">getFilterValues</a></code>.
Features are then selected via <code>select</code> and <code>val</code>.

filterFeatures

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.

filterFeatures: Filter features by thresholding filter values.

Description

Usage

Arguments

Value

See Also