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xgboost
xgboost: A Gradient Boosting Library
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Creater: Tianqi Chen: tianqi.tchen AT gmail
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Goal: A stand-alone efficient library to do learning via boosting in functional space
Features:
(1) 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.
(2) Layout of gradient boosting algorithm to support generic tasks, see project wiki.
* 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:
(1) Gradient boosting models:
* Gradient boosting models:
- regression tree (GBRT)
- linear model/lasso
(2) Objectives to support tasks:
* Objectives to support tasks:
- regression
- classification
- ranking