74db9757b3
* [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
21 lines
824 B
Markdown
21 lines
824 B
Markdown
XGBoost R Feature Walkthrough
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====
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* [Basic walkthrough of wrappers](basic_walkthrough.R)
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* [Train a xgboost model from caret library](caret_wrapper.R)
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* [Cutomize loss function, and evaluation metric](custom_objective.R)
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* [Boosting from existing prediction](boost_from_prediction.R)
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* [Predicting using first n trees](predict_first_ntree.R)
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* [Generalized Linear Model](generalized_linear_model.R)
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* [Cross validation](cross_validation.R)
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* [Create a sparse matrix from a dense one](create_sparse_matrix.R)
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* [Use GPU-accelerated tree building algorithms](gpu_accelerated.R)
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Benchmarks
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====
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* [Starter script for Kaggle Higgs Boson](../../demo/kaggle-higgs)
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Notes
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====
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* Contribution of examples, benchmarks is more than welcomed!
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* If you like to share how you use xgboost to solve your problem, send a pull request:)
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