Who Cited It

XGBoost

2016 · 52,304 citations · 10 from inside this corpus

No author records on this work.

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

XGBoost (2016)XGBoostRandom Forests (2001)Random ForestsGreedy function approximation: A gradient boosting machine. (2001)Greedy function approximation…Scikit-learn: Machine Learning in Python (2011)Scikit-learn: Machine Learnin…From local explanations to global understanding with explainable AI for trees (2020)From local explanations to gl…The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy i… (2020)The advantages of the Matthew…Ensemble learning: A survey (2018)Ensemble learning: A surveyExplainable AI: A Review of Machine Learning Interpretability Methods (2020)Explainable AI: A Review of M…A comparative analysis of gradient boosting algorithms (2020)A comparative analysis of gra…A survey on semi-supervised learning (2019)A survey on semi-supervised l…CatBoost for big data: an interdisciplinary review (2020)CatBoost for big data: an int…TabNet: Attentive Interpretable Tabular Learning (2021)TabNet: Attentive Interpretab…Over-the-Air Deep Learning Based Radio Signal Classification (2018)Over-the-Air Deep Learning Ba…Automated Machine Learning (2019)Automated Machine Learning
13 of 13 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
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What this paper cites, inside the corpus

PaperYearCited
Random Forests2001131,109
Greedy function approximation: A gradient boosting machine.200130,137
Scikit-learn: Machine Learning in Python20118,470

What cites it, inside the corpus

PaperYearCited
From local explanations to global understanding with explainable AI for trees20209,936
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy i…20206,132
Ensemble learning: A survey20183,088
Explainable AI: A Review of Machine Learning Interpretability Methods20202,907
A comparative analysis of gradient boosting algorithms20202,692
A survey on semi-supervised learning20192,636
CatBoost for big data: an interdisciplinary review20201,776
TabNet: Attentive Interpretable Tabular Learning20211,700
Over-the-Air Deep Learning Based Radio Signal Classification20181,604
Automated Machine Learning20191,451

Links

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Topics

Machine Learning and Data ClassificationComputer Science
Machine Learning and AlgorithmsComputer Science
Algorithms and Data CompressionComputer Science

Is this record sound?

partial

One field of this record is missing or disagrees with another. What is shown below is what the source publishes.

  • weakensThe source lists no authors for this work at all, so there is nobody to attribute it to and it appears on no author page.
  • supports21 reference(s) recorded.
  • neutralThe DOI carries no year to check against.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:40+00:00.

sha256 7e3d99a592f7f61f…