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

Other available revisons

Version	Changelog	Date
v0.4.0	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	May 12, 2015, 08:57:16
v0.3.95	New features in the lastest changes Distributed version now runs on Hadoop YARN	March 9, 2015, 23:17:29
v0.3.9	New features in the lastest changes Distributed version that scale xgboost to even larger problems with cluster Feature importance visualization in R module Predict leaf index	January 21, 2015, 19:33:24
v0.3.0	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.	September 2, 2014, 02:43:31
v0.2	New features: - Python interface - New objectives: weighted training, pairwise rank, multiclass softmax - Comes with example script on Kaggle Higgs competition, 20 times faster than skilearn's GBRT	May 17, 2014, 07:27:59
v0.1	Initial Announcement on mloss.org.	March 27, 2014, 07:09:52

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