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metaEnsembleR

Intuitive Package for Heterogeneous Ensemble Meta-Learning

Table of Contents

Overview

metaEnsembleR is a package for meta-level ensemble learning (Classification, Regression) that is fully-automated. It significantly lowers the barrier for the practitioners to apply heterogeneous ensemble learning techniques in an amateur fashion to their everyday predictive problems. Comprehensive demonstrations can be found in Demo.R file, to see the results run Rscript Demo.R in the terminal.

How to start

Install from GitHub :  devtools::install_github(repo = 'ajayarunachalam/metaEnsembleR', ref = 'main')

Install from Rconsole :  install.packages("metaEnsembleR")

Examples

demo classification

library("metaEnsembleR")
attach(iris)
data("iris")
unseen_new_data_testing <- iris[130:150,]
write.csv(unseen_new_data_testing, 'unseen_check.csv', fileEncoding = 'UTF-8', row.names = F)
ensembler_return <- ensembler.classifier(iris[1:130,], 5, c('treebag','rpart'), 'gbm', 0.60, 0.20, 0.20, unseen_new_data_testing) # read.csv('./unseen_check.csv') 


testpreddata <- data.frame(ensembler_return[1])
table(testpreddata$actual_label)
table(ensembler_return[2])

####Performance comparison#####
modelresult <- ensembler_return[3]
modelresult
act_mybar <- qplot(testpreddata$actual_label, geom="bar")
act_mybar
pred_mybar <- qplot(testpreddata$predictions, geom='bar')
pred_mybar
act_tbl <- tableGrob(t(summary(testpreddata$actual_label)))
pred_tbl <- tableGrob(t(summary(testpreddata$predictions)))
ggsave("testdata_actual_vs_predicted_chart.pdf",grid.arrange(act_tbl, pred_tbl))
ggsave("testdata_actual_vs_predicted_plot.pdf",grid.arrange(act_mybar, pred_mybar))
####unseen data###
unseenpreddata <- data.frame(ensembler_return[4])
table(unseenpreddata$unseenpreddata)
table(unseen_new_data_testing$Species)

demo regression

library("metaEnsembleR")
data("rock")
unseen_rock_data <- rock[30:48,]
ensembler_return <- ensembler.regression(rock[1:30,], 4,c('lm'), 'rf', 0.40, 0.30, 0.30, unseen_rock_data)
testpreddata <- data.frame(ensembler_return[1])


####Performance comparison#####
modelresult <- ensembler_return[3]
modelresult
write.csv(modelresult[[1]], "performance_chart.csv")

####unseen data###
unseenpreddata <- data.frame(ensembler_return[4])

Contact

If there is some implementation you would like to see here or add in some examples feel free to do so. You can reach me at email