compute_wanbia_weights: Compute WANBIA weights. Computes feature weights by...
In bnclassify: Learning Discrete Bayesian Network Classifiers from Data
View source: R/learn-params-wanbia.R
compute_wanbia_weights R Documentation
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
character
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 Nov. 16, 2022, 5:08 p.m.
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View source: R/learn-params-wanbia.R
| compute_wanbia_weights | R Documentation |
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 |
character |
dataset |
The data frame from which to learn feature weights |
return_optim_object |
Return full output of 'optim' |
Value
a named numeric vector
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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