An Ensemble Modeling using Random Machines

A novel ensemble method employing Support Vector Machines (SVMs) as base learners. This powerful ensemble model is designed for both classification (Ara A., et. al, 2021) , and regression (Ara A., et. al, 2021) problems, offering versatility and robust performance across different datasets and compared with other consolidated methods as Random Forests (Maia M, et. al, 2021) .

randomMachines

Installation

You can install the development version of randomMachines from GitHub with:

# install.packages("devtools")
devtools::install_github("MateusMaiaDS/randomMachines")

Example

This is a basic example which shows you how to solve a common binary classification problem:

library(randomMachines)
## Simple classification example
sim_train <- randomMachines::sim_class(n=100)
sim_test <- randomMachines::sim_class(n=100)
rm_mod <- randomMachines::randomMachines(y~.,train = sim_train, B = 25,prob_model = F)
rm_mod_pred <- predict(rm_mod,sim_test)

For a regression task we would have similarly

library(randomMachines)
## Simple regression example
sim_train <- randomMachines::sim_reg1(n=100)
sim_test <- randomMachines::sim_reg1(n=100)
rm_mod <- randomMachines::randomMachines(y~.,train = sim_train,B = 25)
rm_mod_pred <- predict(rm_mod,sim_test)