Filter features by using a numerical importance
  criterion. Calculates numerical importance values for all
  features. Thresholding of these values can be used to
  select <dquote>useful</dquote> features. Look at package
  FSelector for details on the filter algorithms.

filterFeatures

Interface to a large number of classification and
        regression techniques, including machine-readable parameter
        descriptions. 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. Nested resampling.

Bernd Bischl

mlr: Machine Learning in R.

filterFeatures function

Filter features by using a numerical importance criterion.
Calculates numerical importance values for all features.
Thresholding of these values can be used to select <dquote>useful</dquote> features.
Look at package FSelector for details on the filter algorithms.

filterFeatures: Filter features by using a numerical importance criterion. Calculates numerical importance values for all features. Thresholding of these values can be used to select useful features. Look at package FSelector for details on the filter algorithms.

Description

Usage

Arguments

Value