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https://github.com/ajayarunachalam/metaensembler
Intuitive Package for Heterogeneous Ensemble Meta-Learning (Classification, Regression) that is fully-automated
https://github.com/ajayarunachalam/metaensembler
automation classification ensemble-machine-learning ensemble-methods generalization-error heterogenous meta-learning predictive-modeling regression stacked-ensembles
Last synced: 8 days ago
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Intuitive Package for Heterogeneous Ensemble Meta-Learning (Classification, Regression) that is fully-automated
- Host: GitHub
- URL: https://github.com/ajayarunachalam/metaensembler
- Owner: ajayarunachalam
- License: gpl-3.0
- Created: 2020-10-26T21:31:04.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2020-10-28T19:14:53.000Z (about 4 years ago)
- Last Synced: 2024-11-06T22:08:19.255Z (about 2 months ago)
- Topics: automation, classification, ensemble-machine-learning, ensemble-methods, generalization-error, heterogenous, meta-learning, predictive-modeling, regression, stacked-ensembles
- Language: R
- Homepage:
- Size: 502 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# metaEnsembleR
Intuitive Package for Heterogeneous Ensemble Meta-Learning
## Table of Contents
- [Overview](#description)
* [How to start](#installation)
* [Examples](#examples)
* [demo classification] (#demo classification)
* [demo regression] (#demo regression)
* [Contact](#contact)
## 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](mailto:[email protected])