33 lines
1.1 KiB
Markdown
33 lines
1.1 KiB
Markdown
xgboost: A Gradient Boosting Library
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Creater: Tianqi Chen: tianqi.tchen AT gmail
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General Purpose Gradient Boosting Library
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Goal: A stand-alone efficient library to do learning via boosting in functional space
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Features:
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* 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.
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* Layout of gradient boosting algorithm to support generic tasks, see project wiki.
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Planned key components:
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* Gradient boosting models:
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- regression tree (GBRT)
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- linear model/lasso
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* Objectives to support tasks:
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- regression
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- classification
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- ranking
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- matrix factorization
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- structured prediction
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(3) OpenMP implementation(optional)
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File extension convention:
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(1) .h are interface, utils and data structures, with detailed comment;
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(2) .cpp are implementations that will be compiled, with less comment;
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(3) .hpp are implementations that will be included by .cpp, with less comment
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See also: https://github.com/tqchen/xgboost/wiki
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