compute_wanbia_weights: Compute WANBIA weights. Computes feature weights by...

In bnclassify: Learning Discrete Bayesian Network Classifiers from Data

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

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.

Usage

compute_wanbia_weights(class, dataset, return_optim_object = FALSE)

Arguments

class	A character. Name of the class variable.
dataset	The data frame from which to learn feature weights
return_optim_object	Return full output of 'optim'

Value

a named numeric vector

bnclassify documentation built on June 22, 2024, 11:08 a.m.