Description:

xgboost: eXtreme Gradient Boosting

It is an efficient and scalable implementation of gradient boosting framework. The package includes efficient linear model solver and tree learning algorithm. The package can automatically do parallel computation with OpenMP, and it can be more than 10 times faster than existing gradient boosting packages such as gbm or sklearn.GBM . It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that user are also allowed to define there own objectives easily.

Changes to previous version:

New features: - R support that is now on CRAN

• Faster tree construction module

• Support for boosting from initial predictions

• Linear booster is now parallelized, using parallel coordinated descent.

BibTeX Entry: Download

Supported Operating Systems: Linux, Windows, Mac Os X

Data Formats: R, Numpy, Libsvm

Tags: Parallel, Gradient Boosting, Tree, Ensemble Learning

Archive: download here

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