evaluation.goodness

Evaluation predictions of a classification model according to Goodness index.

Contains functions to simplify the use of data mining methods (classification, regression, clustering, etc.), for students and beginners in R programming. Various R packages are used and wrappers are built around the main functions, to standardize the use of data mining methods (input/output): it brings a certain loss of flexibility, but also a gain of simplicity. The package name came from the French "Fouille de Données en Master 2 Informatique Décisionnelle".

Alexandre Blansch<c3><a9>

fdm2id

Data Mining and R Programming for Beginners

Alexandre Blansché

evaluation.goodness function

<dl><dt>predictions</dt>
<dd>The predictions of a classification model (<code>factor</code> or <code>vector</code>).</dd>
<dt>gt</dt>
<dd>The ground truth (<code>factor</code> or <code>vector</code>).</dd>
<dt>beta</dt>
<dd>The weight given to precision.</dd>
<dt>positive</dt>
<dd>The label of the positive class.</dd>
<dt>...</dt>
<dd>Other parameters.</dd></dl>

Arguments

Goodness — evaluation.goodness

<dl>

<dt>predictions</dt>
<dd>The predictions of a classification model (<code>factor</code> or <code>vector</code>).</dd>


<dt>gt</dt>
<dd>The ground truth (<code>factor</code> or <code>vector</code>).</dd>


<dt>beta</dt>
<dd>The weight given to precision.</dd>


<dt>positive</dt>
<dd>The label of the positive class.</dd>


<dt>...</dt>
<dd>Other parameters.</dd>

</dl>

evaluation.goodness: Goodness

Description

Usage

Value

Arguments

See Also

Examples