sbfc-package {sbfc}

Selective Bayesian Forest Classifier

Description

An MCMC algorithm for simultaneous feature selection and classification, and visualization of the selected features and feature interactions. An implementation of SBFC by Krakovna, Du and Liu (2015), <arXiv:1506.02371>.

Details

Package:	sbfc
Type:	Package
Title:	Selective Bayesian Forest Classifier
Version:	1.0.3
Date:	2022-01-15
Author:	Viktoriya Krakovna
Maintainer:	Viktoriya Krakovna <[e-mail]>
URL:	https://github.com/vkrakovna/sbfc
BugReports:	https://github.com/vkrakovna/sbfc/issues
Description:	An MCMC algorithm for simultaneous feature selection and classification, and visualization of the selected features and feature interactions. An implementation of SBFC by Krakovna, Du and Liu (2015), <arXiv:1506.02371>.
License:	GPL (>= 2)
Depends:	R (>= 2.10), DiagrammeR
Imports:	Rcpp (>= 0.12.2), Matrix, discretization
LinkingTo:	Rcpp, RcppArmadillo
RoxygenNote:	7.1.0
LazyData:	true

Index of help topics:

corral_augmented        Augmented corral data set: synthetic data with
                        correlated attributes augmented with noise
                        features
data_disc               Data set discretization and formatting
edge_density_plot       Plots the density of edges in a given group
                        over the MCMC iterations
heart                   Heart disease data set: disease outcomes given
                        health attributes
logposterior_plot       Log posterior plot
madelon                 Madelon data set: synthetic data from NIPS 2003
                        feature selection challenge
sbfc                    Selective Bayesian Forest Classifier (SBFC)
                        algorithm
sbfc-package            Selective Bayesian Forest Classifier
sbfc_graph              SBFC graph
signal_size_plot        Trace plot of Group 1 size
signal_var_proportion   Signal variable proportion

Run the SBFC algorithm on a data set using the sbfc function. Make SBFC graphs based on the MCMC samples using the sbfc_graph function. Other analysis, e.g. feature selection plots using signal_var_proportion (based on how often each variable appeared in the signal group).

Author(s)

Viktoriya Krakovna Maintainer: Viktoriya Krakovna <[e-mail]>