Project details for XGBoost

Logo XGBoost v0.4.0

by crowwork - May 12, 2015, 08:57:16 CET [ Project Homepage BibTeX Download ]

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  • 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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