91537e7353
* Fix #3342 and h2oai/h2o4gpu#625: Save predictor parameters in model file This allows pickled models to retain predictor attributes, such as 'predictor' (whether to use CPU or GPU) and 'n_gpu' (number of GPUs to use). Related: h2oai/h2o4gpu#625 Closes #3342. TODO. Write a test. * Fix lint * Do not load GPU predictor into CPU-only XGBoost * Add a test for pickling GPU predictors * Make sample data big enough to pass multi GPU test * Update test_gpu_predictor.cu
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.