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Xgboost

Xgboost

Scalable machine learning system for tree boosting

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. XGBoost originates from research project at University of Washington.

Timeline

March 27, 2014
First release: XGBoost with regression and binary classification support

Further Resources

Title
Author
Link
Type
Date

XGBoost: A Scalable Tree Boosting System

Tianqi Chen, Carlos Guestrin

Research Paper

March 9, 2016

References

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