xgboost: A Gradient Boosting Library

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