CBARuleModelAccuracy

Compares predictions with true labels and outputs accuracy.

Implements the Classification-based on
Association Rules (CBA) algorithm for association rule classification.
The package, also described in Hahsler et al. (2019) <doi:10.32614/RJ-2019-048>,
contains several convenience methods that allow to automatically
set CBA parameters (minimum confidence, minimum support) and it also natively
handles numeric attributes by integrating a pre-discretization step.
The rule generation phase is handled by the 'arules' package.
To further decrease the size of the CBA models produced by the 'arc' package, postprocessing by the
'qCBA' package is suggested.

Tomas Kliegr

Association Rule Classification

CBARuleModelAccuracy function

<dl><dt>prediction</dt>
<dd>vector with predictions</dd>
<dt>groundtruth</dt>
<dd>vector with true labels</dd></dl>

Arguments

Prediction Accuracy — CBARuleModelAccuracy

<dl>

<dt>prediction</dt>
<dd>vector with predictions</dd>


<dt>groundtruth</dt>
<dd>vector with true labels</dd>

</dl>

CBARuleModelAccuracy: Prediction Accuracy

Description

Usage

Value

Arguments