Description:

• Easily accessible in python, R, Julia, CLI
• Fast speed and memory efficient
• Can be more than 10 times faster than GBM in sklearn and R
• Handles sparse matrices, support external memory
• Accurate prediction, and used extensively by data scientists and kagglers
• See highlight links
• Distributed and Portable
• The distributed version runs on Hadoop (YARN), MPI, SGE etc.
• Scales to billions of examples and beyond

Changes to previous version:

• Distributed version of xgboost that runs on YARN, scales to billions of examples

• Direct save/load data and model from/to S3 and HDFS

• Feature importance visualization in R module, by Michael Benesty

• Predict leaf index

• Poisson regression for counts data

• Early stopping option in training

• Native save load support in R and python

• xgboost models now can be saved using save/load in R

• xgboost python model is now pickable

• sklearn wrapper is supported in python module

• Experimental External memory version

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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