compute_wanbia_weights

Compute WANBIA weights. 
Computes feature weights by optimizing conditional log-likelihood. 
Weights are bounded to [0, 1]. Implementation based on the original paper 
and the code provided at <a href="https://sourceforge.net/projects/rawnaivebayes">https://sourceforge.net/projects/rawnaivebayes</a>.

internal

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

compute_wanbia_weights 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 feature weights</dd>
<dt>return_optim_object</dt>
<dd>Return full output of `optim`</dd></dl>

Arguments

Compute WANBIA weights. 
Computes feature weights by optimizing conditional log-likelihood. 
Weights are bounded to [0, 1]. Implementation based on the original paper 
and the code provided at <a href='https://sourceforge.net/projects/rawnaivebayes'>https://sourceforge.net/projects/rawnaivebayes</a>.

Compute WANBIA weights.
 
Computes feature weights by optimizing conditional log-likelihood.  
Weights are bounded to [0, 1]. Implementation based on the original paper 
and the code provided at https://sourceforge.net/projects/rawnaivebayes. — compute_wanbia_weights

<dl>

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


<dt>dataset</dt>
<dd>The data frame from which to learn feature weights</dd>


<dt>return_optim_object</dt>
<dd>Return full output of `optim`</dd>

</dl>

Compute WANBIA weights.
 
Computes feature weights by optimizing conditional log-likelihood. 
Weights are bounded to [0, 1]. Implementation based on the original paper 
and the code provided at <a href='https://sourceforge.net/projects/rawnaivebayes'>https://sourceforge.net/projects/rawnaivebayes</a>.

Compute WANBIA weights.
 
Computes feature weights by optimizing conditional log-likelihood. 
Weights are bounded to [0, 1]. Implementation based on the original paper 
and the code provided at <a href="https://sourceforge.net/projects/rawnaivebayes">https://sourceforge.net/projects/rawnaivebayes</a>.

compute_wanbia_weights: Compute WANBIA weights. Computes feature weights by optimizing conditional log-likelihood. Weights are bounded to [0, 1]. Implementation based on the original paper and the code provided at https://sourceforge.net/projects/rawnaivebayes.

Description

Usage

Value

Arguments