Constant features can lead to errors in some models and obviously provide
no information in the training set that can be learned from.
With the argument <dquote>perc</dquote>, there is a possibility to also remove
features for which less than <dquote>perc</dquote> percent of the observations
differ from the mode value.

removeConstantFeatures

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.

removeConstantFeatures: Remove constant features from a data set.

Description

Usage

Arguments

Value

See Also