xgboost
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Creater: Tianqi Chen: tianqi.tchen AT gmail

General Purpose Gradient Boosting Library

Goal: A stand-alone efficient library to do learning via boosting in functional space

Features:
(1) Sparse feature format, handling of missing features. This allows efficient categorical feature encoding as indicators. The speed of booster only depens on number of existing features.
(2) Layout of gradient boosting algorithm to support generic tasks, see project wiki.


Planned key components:

(1) Gradient boosting models: 
    - regression tree
    - linear model/lasso
(2) Objectives to support tasks: 
    - regression
    - classification
    - ranking
    - matrix factorization
    - structured prediction
(3) OpenMP implementation(optional)

File extension convention: 
(1) .h are interface, utils and data structures, with detailed comment; 
(2) .cpp are implementations that will be compiled, with less comment; 
(3) .hpp are implementations that will be included by .cpp, with less comment

See also: https://github.com/tqchen/xgboost/wiki