removeConstantFeatures

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

[<code>numeric(1)</code>]
The percentage of a feature values in [0, 1) that must differ from the mode value.
Default is 0, which means only constant features with exactly one observed level are removed.

perc

[<code>character</code>]
Names of the columns which must not be deleted.
Default is no columns.

dont.rm

[<code>logical(1)</code>]
Should NAs be ignored in the percentage calculation?
(Or should they be treated as a single, extra level in the percentage calculation?)
Note that if the feature has only missing values, it is always removed.
Default is <code>FALSE</code>.

na.ignore

[<code>numeric(1)</code>]
Numerical tolerance to treat two numbers as equal.
Variables stored as <code>double</code> will get rounded accordingly before computing the mode.
Default is <code>sqrt(.Maschine$double.eps)</code>.

[<code>logical(1)</code>]
Print verbose output on console?
Default is set via <code><a rd-options="" href="/link/configureMlr?package=mlr&version=2.9" data-mini-rdoc="mlr::configureMlr">configureMlr</a></code>.

show.info


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 &#147;perc&#148;, there is a possibility to also remove
features for which less than &#147;perc&#148; percent of the observations
differ from the mode value.


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

removeConstantFeatures function

[<code>data.frame</code> | <code><a rd-options='' href='Task'>Task</a></code>]
Input data.

[<code>logical(1)</code>]
Print verbose output on console?
Default is set via <code><a rd-options='' href='configureMlr'>configureMlr</a></code>.

removeConstantFeatures: Remove constant features from a data set.

Description

Usage

Arguments

Value

See Also