DEdevy·Dec 8, 2016Submission
XGBoost: Distributed Gradient Boosting (GBDT, GBRT or GBM) Library
2 points · Open discussiondmlc
xgboost
C++2015–2025
dmlc / xgboost
xgboost
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
10Distinct people
10Mentions
0Explicit recommenders
29KGitHub stars
What to know before exploring
What it is
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
No recommendation phrase was found in the indexed comments.
No classified warning is attached to this page. Read the discussions before treating silence as approval.
Also in these discussions
No other repository is repeatedly linked in the same discussions.
This week on Hacker News
Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 1:57:11 AM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by year10 dated mentions
Observed mentions: 2015-04-24 17:29:49 UTC to 2025-12-24 05:07:05 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2015 | 2015-01-01 00:00:00 UTC | 2016-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2016 | 2016-01-01 00:00:00 UTC | 2017-01-01 00:00:00 UTC | 3 | 1 | 2 |
| 2017 | 2017-01-01 00:00:00 UTC | 2018-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2018 | 2018-01-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2019 | 2019-01-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 2 | 2 | 0 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 2 | 2 | 0 |
| 2024 | 2024-01-01 00:00:00 UTC | 2025-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2025 | 2025-01-01 00:00:00 UTC | 2026-01-01 00:00:00 UTC | 1 | 1 | 0 |
What Hacker News says
3 sourcesIf it actually bothers you, then do something about it.
The author of these slides provided two versions of this material, one in prose (linked from my other comment in this thread) and these slides. Send a pull request to the XGBoost GitHub repo to update the prose version[1]. Once you're done with that, send an email to the author with the new images, non-abrasively explain why you're providing them, and politely ask the author to replace the slides on his website. Be sure to point out which slides need to be updated (8, 9, 24, 25, 28, 31) and which images go with which slides.
It's possible your email will get ignored (PhD students are busy and don't necessarily have time to maintain two year old teaching material), but I'm almost certain the pull request will be merged.
[1] https://github.com/dmlc/xgboost/blob/master/doc/model.md
PS:
Read the rest
If you don't do this, I'll call you tone-deaf and lazy. ;)
May I suggest Kaggle?
The AllenAI Institute is currently running a pretty interesting contest there at the moment, with pretty direct links to OpenAI's mission.
Of course Kaggle often degenerates into a "who can drive xgboost[1] the best", but some of the feature engineering is quite novel.
A decent Winograd schema challenge would be pretty interesting.
[1] https://github.com/dmlc/xgboost#whats-new
In AMA: the OpenAI research team