What is recorded here?
Described a scalable end-to-end tree-boosting system with a sparsity-aware algorithm, weighted quantile sketch and systems techniques for cache use, compression and sharding.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The record is the exact two-author 2016 paper and its reported design and evaluations. It does not assign all gradient-boosting theory, later implementations, deployments or universal performance superiority to this work.
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People and systems
Tianqi Chen; Carlos Guestrin
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.
Research locations and affiliations
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Documented countries / regions: United States
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English catalogue narrative
Chen and Guestrin: XGBoost
Described a scalable end-to-end tree-boosting system with a sparsity-aware algorithm, weighted quantile sketch and systems techniques for cache use, compression and sharding.
Scientific scope
The record is the exact two-author 2016 paper and its reported design and evaluations. It does not assign all gradient-boosting theory, later implementations, deployments or universal performance superiority to this work.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Tianqi Chen; Carlos Guestrin
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.