Package: stackgbm 0.1.0
stackgbm: Stacked Gradient Boosting Machines
A minimalist implementation of model stacking by Wolpert (1992) <doi:10.1016/S0893-6080(05)80023-1> for boosted tree models. A classic, two-layer stacking model is implemented, where the first layer generates features using gradient boosting trees, and the second layer employs a logistic regression model that uses these features as inputs. Utilities for training the base models and parameters tuning are provided, allowing users to experiment with different ensemble configurations easily. It aims to provide a simple and efficient way to combine multiple gradient boosting models to improve predictive model performance and robustness.
Authors:
stackgbm_0.1.0.tar.gz
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stackgbm_0.1.0.tgz(r-4.4-any)stackgbm_0.1.0.tgz(r-4.3-any)
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stackgbm.pdf |stackgbm.html✨
stackgbm/json (API)
NEWS
| # Install 'stackgbm' in R: |
| install.packages('stackgbm', repos = c('https://nanxstats.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/nanxstats/stackgbm/issues
automlcatboostdecision-treesensemble-learninggbdtgbmgradient-boostinglightgbmmachine-learningmodel-stackingxgboost
Last updated 5 months agofrom:db33de5656. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 13 2024 |
| R-4.5-win | OK | Sep 13 2024 |
| R-4.5-linux | OK | Sep 13 2024 |
| R-4.4-win | OK | Sep 13 2024 |
| R-4.4-mac | OK | Sep 13 2024 |
| R-4.3-win | OK | Sep 13 2024 |
| R-4.3-mac | OK | Sep 13 2024 |
Exports:catboost_load_poolcatboost_predictcatboost_traincv_catboostcv_lightgbmcv_param_gridcv_xgboostis_installed_catboostis_installed_lightgbmis_installed_xgboostlightgbm_trainstackgbmxgboost_dmatrixxgboost_train
Dependencies:clicrayongluehmslifecyclepkgconfigplyrprettyunitspROCprogressR6Rcpprlangvctrs
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Create a dataset | catboost_load_pool |
| Predict based on the model | catboost_predict |
| Train the model | catboost_train |
| catboost - parameter tuning and model selection with k-fold cross-validation and grid search | cv_catboost |
| lightgbm - parameter tuning and model selection with k-fold cross-validation and grid search | cv_lightgbm |
| Generate a parameter grid for cross-validation | cv_param_grid |
| xgboost - parameter tuning and model selection with k-fold cross-validation and grid search | cv_xgboost |
| Is catboost installed? | is_installed_catboost |
| Is lightgbm installed? | is_installed_lightgbm |
| Is xgboost installed? | is_installed_xgboost |
| Train lightgbm model | lightgbm_train |
| Make predictions from a stackgbm model object | predict.stackgbm |
| Model stacking for boosted trees | stackgbm |
| Create xgb.DMatrix object | xgboost_dmatrix |
| Train xgboost model | xgboost_train |
