Package: randomMachines 0.1.0
randomMachines: 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) <doi:10.6339/21-JDS1014>, and regression (Ara A., et. al, 2021) <doi:10.1016/j.eswa.2022.117107> problems, offering versatility and robust performance across different datasets and compared with other consolidated methods as Random Forests (Maia M, et. al, 2021) <doi:10.6339/21-JDS1025>.
Authors:
randomMachines_0.1.0.tar.gz
randomMachines_0.1.0.zip(r-4.5)randomMachines_0.1.0.zip(r-4.4)randomMachines_0.1.0.zip(r-4.3)
randomMachines_0.1.0.tgz(r-4.4-any)randomMachines_0.1.0.tgz(r-4.3-any)
randomMachines_0.1.0.tar.gz(r-4.5-noble)randomMachines_0.1.0.tar.gz(r-4.4-noble)
randomMachines_0.1.0.tgz(r-4.4-emscripten)randomMachines_0.1.0.tgz(r-4.3-emscripten)
randomMachines.pdf |randomMachines.html✨
randomMachines/json (API)
| # Install 'randomMachines' in R: |
| install.packages('randomMachines', repos = c('https://mateusmaiads.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mateusmaiads/randommachines/issues
- bolsafam - Bolsa Família Dataset
- ionosphere - Ionosphere Dataset
- whosale - Wholesale Dataset
Last updated 8 months agofrom:5ba69d47f3. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 10 2024 |
| R-4.5-win | OK | Nov 10 2024 |
| R-4.5-linux | OK | Nov 10 2024 |
| R-4.4-win | OK | Nov 10 2024 |
| R-4.4-mac | OK | Nov 10 2024 |
| R-4.3-win | OK | Nov 10 2024 |
| R-4.3-mac | OK | Nov 10 2024 |
Exports:brier_scorerandomMachinesRMSEsim_classsim_reg1sim_reg2sim_reg3sim_reg4sim_reg5
Dependencies:kernlab
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Bolsa Família Dataset | bolsafam |
| Brier Score function | brier_score |
| Ionosphere Dataset | ionosphere |
| Prediction function for the rm_class_model | predict,rm_class-method predict.rm_class |
| Prediction function for the rm_reg_model | predict,rm_reg-method predict.rm_reg |
| Random Machines | randomMachines |
| S4 class for RM classification | rm_class rm_class-class |
| S4 class for RM regression | rm_reg rm_reg-class |
| Root Mean Squared Error (RMSE) Function | RMSE |
| Generate a binary classification data set from normal distribution | sim_class |
| Simulation for a regression toy examples from Random Machines Regression 1 | sim_reg1 |
| Simulation for a regression toy examples from Random Machines Regression 2 | sim_reg2 |
| Simulation for a regression toy examples from Random Machines Regression 3 | sim_reg3 |
| Simulation for a regression toy examples from Random Machines Regression 3 | sim_reg4 |
| Simulation for a regression toy examples from Random Machines Regression 3 | sim_reg5 |
| Wholesale Dataset | whosale |
