Learn a naive Bayes network structure.

State-of-the art algorithms for learning discrete Bayesian network classifiers from data, including a number of those described in Bielza & Larranaga (2014) <doi:10.1145/2576868>, with functions for prediction, model evaluation and inspection.

Mihaljevic Bojan

bnclassify

Learning Discrete Bayesian Network Classifiers from Data

Bielza Concha

Larranaga Pedro

Wickham Hadley

nb function

<dl><dt>class</dt>
<dd>A character. Name of the class variable.</dd>
<dt>dataset</dt>
<dd>The data frame from which to learn the classifier.</dd>
<dt>features</dt>
<dd>A character vector. The names of the features. This argument
is ignored if <code>dataset</code> is provided.</dd></dl>

Arguments

Learn a naive Bayes network structure. — nb

<dl>

<dt>class</dt>
<dd>A character. Name of the class variable.</dd>


<dt>dataset</dt>
<dd>The data frame from which to learn the classifier.</dd>


<dt>features</dt>
<dd>A character vector. The names of the features. This argument
is ignored if <code>dataset</code> is provided.</dd>

</dl>

nb: Learn a naive Bayes network structure.

Description

Usage

Value

Arguments

Examples