makeConstantClassWrapper

[<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

[numeric(1)]
The fraction of labels in [0, 1) that can be different from the majority label. Default is 0, which means that constant labels are only predicted if there is exactly one label in the data.

frac


If the training data contains only a single class (or almost only a single class), this wrapper creates a model that always predicts the constant class in the training data. In all other cases, the underlying learner is trained and the resulting model used for predictions.<p></p><p>Probabilities can be predicted and will be 1 or 0 depending on whether the label matches the majority class or not.
</p>

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

makeConstantClassWrapper 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>.


If the training data contains only a single class (or almost only a single class), this wrapper creates a model that always predicts the constant class in the training data. In all other cases, the underlying learner is trained and the resulting model used for predictions.

Probabilities can be predicted and will be 1 or 0 depending on whether the label matches the majority class or not.


makeConstantClassWrapper: Wraps a classification learner to support problems where the class label is (almost) constant.

Description

Usage

Arguments

Value

See Also