5879acde9a
* [CI] Move lint to a separate script * [CI] Improved lintr launcher * Add lintr as a separate action * Add custom parsing logic to print out logs * Fix lintr issues in demos * Run R demos * Fix CRAN checks * Install XGBoost into R env before running lintr * Install devtools (needed to run demos)
825 B
825 B
XGBoost R Feature Walkthrough
- Basic walkthrough of wrappers
- Train a xgboost model from caret library
- Cutomize loss function, and evaluation metric
- Boosting from existing prediction
- Predicting using first n trees
- Generalized Linear Model
- Cross validation
- Create a sparse matrix from a dense one
- Use GPU-accelerated tree building algorithms
Benchmarks
Notes
- Contribution of examples, benchmarks is more than welcomed!
- If you like to share how you use xgboost to solve your problem, send a pull request :)