c18a3660fa
* add back train method but mark as deprecated * add back train method but mark as deprecated * add back train method but mark as deprecated * fix scalastyle error * fix scalastyle error * fix scalastyle error * fix scalastyle error * init * more changes * temp * update * udpate rabit * change the histogram * update kfactor * sync per node stats * temp * update * final * code clean * update rabit * more cleanup * fix errors * fix failed tests * enforce c++11 * broadcast subsampled feature correctly * init col * temp * col sampling * fix histmastrix init * fix col sampling * remove cout * fix out of bound access * fix core dump remove core dump file * disbale test temporarily * update * add fid * print perf data * update * revert some changes * temp * temp * pass all tests * bring back some tests * recover some changes * fix lint issue * enable monotone and interaction constraints * don't specify default for monotone and interactions * recover column init part * more recovery * fix core dumps * code clean * revert some changes * fix test compilation issue * fix lint issue * resolve compilation issue * fix issues of lint caused by rebase * fix stylistic changes and change variable names * use regtree internal function * modularize depth width * address the comments * fix failed tests * wrap perf timers with class * fix lint * fix num_leaves count * fix indention * Update src/tree/updater_quantile_hist.cc Co-Authored-By: CodingCat <CodingCat@users.noreply.github.com> * Update src/tree/updater_quantile_hist.h Co-Authored-By: CodingCat <CodingCat@users.noreply.github.com> * Update src/tree/updater_quantile_hist.cc Co-Authored-By: CodingCat <CodingCat@users.noreply.github.com> * Update src/tree/updater_quantile_hist.cc Co-Authored-By: CodingCat <CodingCat@users.noreply.github.com> * Update src/tree/updater_quantile_hist.cc Co-Authored-By: CodingCat <CodingCat@users.noreply.github.com> * Update src/tree/updater_quantile_hist.h Co-Authored-By: CodingCat <CodingCat@users.noreply.github.com> * merge * fix compilation
eXtreme Gradient Boosting
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XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.
License
© Contributors, 2016. Licensed under an Apache-2 license.
Contribute to XGBoost
XGBoost has been developed and used by a group of active community members. Your help is very valuable to make the package better for everyone. Checkout the Community Page
Reference
- Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016
- XGBoost originates from research project at University of Washington.