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* [R] MSVC compatibility * [GPU] allow seed in BernoulliRng up to size_t and scale to uint32_t * R package build with cmake and CUDA * R package CUDA build fixes and cleanups * always export the R package native initialization routine on windows * update the install instructions doc * fix lint * use static_cast directly to set BernoulliRng seed * [R] demo for GPU accelerated algorithm * tidy up the R package cmake stuff * R pack cmake: installs main dependency packages if needed * [R] version bump in DESCRIPTION * update NEWS * added short missing/sparse values explanations to FAQ
824 B
824 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:)