Description:

xgboost: eXtreme Gradient Boosting

This is yet another gradient boosting (tree) (GBRT) library.

The implementation is based on C++, optimized for memory and efficiency, to try target large data with a single machine.

Palatalization is done with OpenMP. The algorithm relies on sparse feature format, which means it naturally handles missing values.

Supported key components so far:

• Gradient boosting models:

  • regression tree (GBRT)

  • linear model/lasso

• Objectives to support tasks:

  • regression

  • classification

  • rank

• OpenMP implementation

Changes to previous version:

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

BibTeX Entry: Download

Supported Operating Systems: Linux, Mac Os X

Data Formats: Numpy, Libsvm

Tags: Parallel, Gradient Boosting, Tree, Ensemble Learning

Archive: download here

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