This function builds a classification model using Naive Bayes.

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

NB function

<dl><dt>train</dt>
<dd>The training set (description), as a <code>data.frame</code>.</dd>
<dt>labels</dt>
<dd>Class labels of the training set (<code>vector</code> or <code>factor</code>).</dd>
<dt>tune</dt>
<dd>If true, the function returns parameters instead of a classification model.</dd>
<dt>methodparameters</dt>
<dd>Present for interface consistency with <code>performance</code>
(which always passes it when fitting a model). Currently unused: <code>NB</code> does not
support reusing pre-tuned parameters.</dd>
<dt>graph</dt>
<dd>Present for interface consistency with <code>performance</code> (which always
passes it when fitting a model). Currently unused: <code>NB</code> does not produce a plot.</dd>
<dt>seed</dt>
<dd>A specified seed for random number generation, so that two runs on the same
data give the same model. Every learning method accepts it, so that it can be set the same
way whatever the method; the deterministic ones simply have nothing to draw and give the
same model with or without it.</dd>
<dt>...</dt>
<dd>Other parameters.</dd></dl>

Arguments

Classification using Naive Bayes — NB

<dl>

<dt>train</dt>
<dd>The training set (description), as a <code>data.frame</code>.</dd>


<dt>labels</dt>
<dd>Class labels of the training set (<code>vector</code> or <code>factor</code>).</dd>


<dt>tune</dt>
<dd>If true, the function returns parameters instead of a classification model.</dd>


<dt>methodparameters</dt>
<dd>Present for interface consistency with <code>performance</code>
(which always passes it when fitting a model). Currently unused: <code>NB</code> does not
support reusing pre-tuned parameters.</dd>


<dt>graph</dt>
<dd>Present for interface consistency with <code>performance</code> (which always
passes it when fitting a model). Currently unused: <code>NB</code> does not produce a plot.</dd>


<dt>seed</dt>
<dd>A specified seed for random number generation, so that two runs on the same
data give the same model. Every learning method accepts it, so that it can be set the same
way whatever the method; the deterministic ones simply have nothing to draw and give the
same model with or without it.</dd>


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

</dl>

NB: Classification using Naive Bayes

Description

Usage

Value

Arguments

See Also

Examples