780 B
780 B
xgboost
Creater: Tianqi Chen: tianqi.tchen AT gmail
General Purpose Gradient Boosting Library
Intention: A stand-alone efficient library to do machine learning in functional space
Planned key components (TODO):
(1) Gradient boosting models: - regression tree - linear model/lasso (2) Objectives to support tasks: - regression - classification - ranking - matrix factorization - structured prediction (3) OpenMP support for parallelization(optional)
File extension convention: (1) .h are interface, utils anddata 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
Parameters Usage: see wiki in github