Claim · 2016-03-09 · publication · Stated in source · Supported

The paper describes XGBoost, a scalable machine learning system for tree boosting.

One recorded claim. The claim panel gives its qualification, the source passages with their place in each source, and how it was checked.

What it connects

XGBoost: A Scalable Tree Boosting System → states its contribution → The paper describes XGBoost, a scalable machine learning system for tree boosting.

Where it appears

No collection page holds this claim. It is listed with its records in the explorer.

Claim and evidence

XGBoost: A Scalable Tree Boosting Systemstates its contribution ↓The paper describes XGBoost, a scalable machine learning system for tree boosting.

Machine-checked, not reviewed by an editorStated in sourceSupported

Supporting evidence: primary · 1 source

Date: 2016-03-09 · publication Records dated 2016 →

Scope and limitsThe paper's own description of its contribution; not an assessment of it.

The evidence 01

primary sourceSupports the claim

XGBoost: A Scalable Tree Boosting System

arXiv · 2016-03-09

Cite the original source

Cite the original document, not Selekoa. Check these details against it.

Source excerpt

“In this paper, we describe XGBoost, a scalable machine learning system for tree boosting.”

Location in source arXiv v1, PDF p. 1, §1 Introduction, para. 3

Machine-checked · 2026-10-31

Source details and check notes

Authors: Tianqi Chen; Carlos Guestrin
Version: arXiv:1603.02754v1, submitted 9 March 2016 (UTC). Retrieved for the candidate pack on 2026-10-28 (SHA-256 6efbbcf077f2a0344bddb5777a6cd74263a2860e05e55d4be6ca1db911f7c2b5). The author list is the byline as printed on the first page.
Version date: 2016-03-09
Canonical address: Open the canonical source ↗
Accessed: 2026-10-28
Language: English (en)
Attribution: Tianqi Chen, Carlos Guestrin, “XGBoost: A Scalable Tree Boosting System”, 2016-03-09.
Reuse: No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution.
Check notes: Candidate pack passage, key papers (research/key-papers). The quotation (14 words) matched the text retrieved for the candidate pack on 2026-10-28 (research/key-papers/verification.json). Matching ignored differences of whitespace, case, quotation marks, dashes and line-break hyphens, so the source’s typography can differ. A cross-family machine check (2026-10-30, item B031) found the quotation at its locator. No human editorial review.

Status and meaning

Editorial status. Not reviewed by an editor. An exploratory record that no editor has accepted. This status does not say whether a person has read any linked passage. Each evidence record above shows how it was checked.

Relation. A paper or other publication describes its own contribution, quoted from the expression the record names, usually its abstract or introduction. The record is the document’s own description, not an assessment: a claim of priority or novelty it contains is the document’s claim, flagged as such, and says nothing about earlier or later work.

Date. Date of the arXiv version quoted, submitted 9 March 2016 (UTC); later versions may word it differently.

Record ID kp-states-xgboost-scalable-tree-boosting-system-2016-1