Package: gbts 1.2.0
gbts: Hyperparameter Search for Gradient Boosted Trees
An implementation of hyperparameter optimization for Gradient Boosted Trees on binary classification and regression problems. The current version provides two optimization methods: Bayesian optimization and random search. Instead of giving the single best model, the final output is an ensemble of Gradient Boosted Trees constructed via the method of ensemble selection.
Authors:
gbts_1.2.0.tar.gz
gbts_1.2.0.zip(r-4.5)gbts_1.2.0.zip(r-4.4)gbts_1.2.0.zip(r-4.3)
gbts_1.2.0.tgz(r-4.4-any)gbts_1.2.0.tgz(r-4.3-any)
gbts_1.2.0.tar.gz(r-4.5-noble)gbts_1.2.0.tar.gz(r-4.4-noble)
gbts_1.2.0.tgz(r-4.4-emscripten)gbts_1.2.0.tgz(r-4.3-emscripten)
gbts.pdf |gbts.html✨
gbts/json (API)
NEWS
| # Install 'gbts' in R: |
| install.packages('gbts', repos = c('https://wliang10.r-universe.dev', 'https://cloud.r-project.org')) |
- boston_housing - Boston housing data
- german_credit - German credit data
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 8 years agofrom:8868614fb9. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 14 2024 |
| R-4.5-win | OK | Nov 14 2024 |
| R-4.5-linux | OK | Nov 14 2024 |
| R-4.4-win | OK | Nov 14 2024 |
| R-4.4-mac | OK | Nov 14 2024 |
| R-4.3-win | OK | Nov 14 2024 |
| R-4.3-mac | OK | Nov 14 2024 |
Dependencies:codetoolsdigestdoParalleldoRNGearthforeachFormulagbmiteratorslatticeMatrixplotmoplotrixrngtoolssurvival
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Boston housing data | boston_housing |
| Compute model performance | comperf |
| Hyperparameter Search for Gradient Boosted Trees | gbts-package gbts |
| German credit data | german_credit |
| Predict method for ensemble of Gradient Boosted Trees | predict.gbts |
