makeRemoveConstantFeaturesWrapper

[<code><a rd-options="" href="/link/Learner?package=mlr&version=2.9" data-mini-rdoc="mlr::Learner">Learner</a></code> | <code>character(1)</code>]
The learner.
If you pass a string the learner will be created via <code><a rd-options="" href="/link/makeLearner?package=mlr&version=2.9" data-mini-rdoc="mlr::makeLearner">makeLearner</a></code>.

learner

[<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>.


Fuses a base learner with the preprocessing implemented in <code><a rd-options="" href="/link/removeConstantFeatures?package=mlr&version=2.9" data-mini-rdoc="mlr::removeConstantFeatures">removeConstantFeatures</a></code>.


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

makeRemoveConstantFeaturesWrapper function

[<code><a rd-options='' href='Learner'>Learner</a></code> | <code>character(1)</code>]
The learner.
If you pass a string the learner will be created via <code><a rd-options='' href='makeLearner'>makeLearner</a></code>.


Fuses a base learner with the preprocessing implemented in <code><a rd-options='' href='removeConstantFeatures'>removeConstantFeatures</a></code>.


makeRemoveConstantFeaturesWrapper: Fuse learner with removal of constant features preprocessing.

Description

Usage

Arguments

Value

See Also