getConfVectorForROC

Methods for computing ROC curves require a vector of confidences
of the positive class, while in CBA, the confidence returned by predict with
outputProbabilies = TRUE returns confidence for the predicted class.
This method converts the values to confidences for the positive class

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

getConfVectorForROC function

<dl><dt>confidences</dt>
<dd>Vector of confidences</dd>
<dt>predictedClass</dt>
<dd>Vector with predicted classes</dd>
<dt>positiveClass</dt>
<dd>Positive class (String)</dd></dl>

Arguments

Returns vector with confidences for the positive class (useful for ROC or AUC computation) — getConfVectorForROC

<dl>

<dt>confidences</dt>
<dd>Vector of confidences</dd>


<dt>predictedClass</dt>
<dd>Vector with predicted classes</dd>


<dt>positiveClass</dt>
<dd>Positive class (String)</dd>

</dl>

getConfVectorForROC: Returns vector with confidences for the positive class (useful for ROC or AUC computation)

Description

Usage

Value

Arguments

Examples