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randomMachines  

An Ensemble Modeling using Random Machines
View on CRAN: Click here


Download and install randomMachines package within the R console
Install from CRAN:
install.packages("randomMachines")

Install from Github:
library("remotes")
install_github("cran/randomMachines")

Install by package version:
library("remotes")
install_version("randomMachines", "0.1.0")



Attach the package and use:
library("randomMachines")
Maintained by
Mateus Maia
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-12-14
Latest Update: 2023-12-14
Description:
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) .
How to cite:
Mateus Maia (2023). randomMachines: An Ensemble Modeling using Random Machines. R package version 0.1.0, https://cran.r-project.org/web/packages/randomMachines. Accessed 21 Nov. 2024.
Previous versions and publish date:
No previous versions
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Complete documentation for randomMachines
Functions, R codes and Examples using the randomMachines R package
Some associated functions: RMSE . bolsafam . brier_score . ionosphere . predict.rm_class . predict.rm_reg . randomMachines . rm_class-class . rm_reg-class . sim_class . sim_reg1 . sim_reg2 . sim_reg3 . sim_reg4 . sim_reg5 . whosale . 
Some associated R codes: main_functions.R . simulation_examples.R .  Full randomMachines package functions and examples
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