alexWhitworth/glmEnsemble: Builds GLM ensemble models in parallel
Version 0.2.3

This package trains and tests GLM ensemble models with backward variable selection via AIC. Holdout testing data is first selected and then ensemble elements are trained on training datasets via bootstrap resampling from the non-holdout data. The following family-link functions are supported: binomial-logit, binomial-probit, poisson-log, gaussian-identity. Binomial resample size is controled by the user, while poisson and gaussian data use bagging. Resulting ensemble coefficients are weighted by accuracy on test data. By default, ensemble elements are built in parallel.

Getting started

Package details

Maintainer
LicenseGPL-3
Version0.2.3
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
install.packages("devtools")
library(devtools)
install_github("alexWhitworth/glmEnsemble")
alexWhitworth/glmEnsemble documentation built on June 10, 2017, 3:20 p.m.