oversample

undersample

(<a rd-options="" href="/link/Task?package=mlr&version=2.19.0" data-mini-rdoc="mlr::Task">Task</a>)
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.

Patrick Schratz

Machine Learning in R

Bernd Bischl

Michel Lang

Lars Kotthoff

Julia Schiffner

Jakob Richter

Zachary Jones

Giuseppe Casalicchio

Mason Gallo

Jakob Bossek

Erich Studerus

Leonard Judt

Tobias Kuehn

Pascal Kerschke

Florian Fendt

Philipp Probst

Xudong Sun

Janek Thomas

Bruno Vieira

Laura Beggel

Quay Au

Martin Binder

Florian Pfisterer

Stefan Coors

Steve Bronder

Alexander Engelhardt

Christoph Molnar

Annette Spooner

oversample function

<dl><dt>task</dt>
<dd>(Task) 
The task.</dd>
<dt>rate</dt>
<dd>(<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.</dd>
<dt>cl</dt>
<dd>(<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.</dd></dl>

Arguments

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

<dl>

<dt>task</dt>
<dd>(Task) 
The task.</dd>


<dt>rate</dt>
<dd>(<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.</dd>


<dt>cl</dt>
<dd>(<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.</dd>

</dl>

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

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

Description

Usage

Value

Arguments

See Also