splitForTrainingAndTest

ratio of training and test sets (default: 15% of the data is used for testing)

ratio

Split the input and target values to a training and a test set. Test set is taken from the end of the
data. If the data is to be shuffled, this should be done before calling this function.

The Stuttgart Neural Network Simulator (SNNS) is a library
containing many standard implementations of neural networks. This
package wraps the SNNS functionality to make it available from
within R. Using the 'RSNNS' low-level interface, all of the
algorithmic functionality and flexibility of SNNS can be accessed.
Furthermore, the package contains a convenient high-level
interface, so that the most common neural network topologies and
learning algorithms integrate seamlessly into R.

Christoph Bergmeir

RSNNS

Neural Networks using the Stuttgart Neural Network Simulator
(SNNS)

José M. Benítez

Andreas Zell

Niels Mache

Günter Mamier

Michael Vogt

Sven Döring

Ralf Hübner

Kai-Uwe Herrmann

Tobias Soyez

Michael Schmalzl

Tilman Sommer

Artemis Hatzigeorgiou

Dietmar Posselt

Tobias Schreiner

Bernward Kett

Martin Reczko

Martin Riedmiller

Mark Seemann

Marcus Ritt

Jamie DeCoster

Jochen Biedermann

Joachim Danz

Christian Wehrfritz

Patrick Kursawe

Andre El-Ama

splitForTrainingAndTest function

<dl><dt>x</dt>
<dd>inputs</dd>
<dt>y</dt>
<dd>targets</dd>
<dt>ratio</dt>
<dd>ratio of training and test sets (default: 15% of the data is used for testing)</dd></dl>

Arguments

Function to split data into training and test set — splitForTrainingAndTest

<dl>

<dt>x</dt>
<dd>inputs</dd>


<dt>y</dt>
<dd>targets</dd>


<dt>ratio</dt>
<dd>ratio of training and test sets (default: 15% of the data is used for testing)</dd>

</dl>

Function to split data into training and test set

splitForTrainingAndTest: Function to split data into training and test set

Description

Usage

Value

Arguments

Examples