oversample

undersample

[<code><a rd-options="" href="/link/Task?package=mlr&version=2.10" data-mini-rdoc="mlr::Task">Task</a></code>]
The task.

task

[<code>numeric(1)</code>]
Factor to upsample or downsample a class.
For undersampling: Must be between 0 and 1,
where 1 means no downsampling, 0.5 implies reduction to 50 percent
and 0 would imply reduction to 0 observations.
For oversampling: Must be between 1 and <code>Inf</code>,
where 1 means no oversampling and 2 would mean doubling the class size.

rate

[<code>character(1)</code>]
Which class should be over- or undersampled. If <code>NULL</code>, <code>oversample</code>
will select the smaller and <code>undersample</code> the larger class.

Oversampling: For a given class (usually the smaller one) all existing observations are
taken and copied and extra observations are added by randomly sampling with replacement from this class. Undersampling: For a given class (usually the larger one) the number of observations is
reduced (downsampled) by randomly sampling without replacement from this class.

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

oversample function

[<code><a rd-options='' href='Task'>Task</a></code>]
The task.

oversample: Over- or undersample binary classification task to handle class imbalancy.

Description

Usage

Arguments

Value

See Also