7fefd6865d
* Fix #3402: wrong fid crashes distributed algorithm The bug was introduced by the recent DMatrix refactor (#3301). It was partially fixed by #3408 but the example in #3402 was still failing. The example in #3402 will succeed after this fix is applied. * Explicitly specify "this" to prevent compile error * Add regression test * Add distributed test to Travis matrix * Install kubernetes Python package as dependency of dmlc tracker * Add Python dependencies * Add compile step * Reduce size of regression test case * Further reduce size of test
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.