36 lines
1.0 KiB
Markdown
36 lines
1.0 KiB
Markdown
xgboost: eXtreme Gradient Boosting
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An efficient general purpose gradient boosting (tree) library.
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Creater: Tianqi Chen
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Documentation: https://github.com/tqchen/xgboost/wiki
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Features
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=======
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* Sparse feature format:
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- Sparse feature format allows easy handling of missing values, and improve computation efficiency.
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* Push the limit on single machine:
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- Efficient implementation that optimizes memory and computation.
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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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=======
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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
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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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