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:
- Sparse feature format, handling of missing features. This allows efficient categorical feature encoding as indicators. The speed of booster only depends on number of existing features.
- Layout of gradient boosting algorithm to support generic tasks, see project wiki.
Planned key components:
- Gradient boosting models:
- regression tree (GBRT)
- linear model/lasso
- 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