HAhandfuloflight·Dec 24, 2025Submission
1 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
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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
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Where it is used
No documented product use has been verified yet.
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:03:16 PM 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
10 sourcesHi, thanks for your comment:
I opened an issue on Github. This didn't seem obvious to many people that read the article, so it's nice that now we can keep this in mind while using the model:
https://github.com/dmlc/xgboost/issues/4069
In Can Gradient Boosting Learn Simple Arithmetic?XGBoost: Distributed Gradient Boosting (GBDT, GBRT or GBM) Library
2 points · Open discussionIf 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 teamDay 1: Spend some time setting your machine up for doing machine learning. For Python look at Numpy, IPython Notebook, Scipy, Pandas, Scikit-Learn, Matplotlib, Seaborn, NLTK, XGBoost wrapper, Vowpal Wabbit wrapper, Theano + Nolearn.
2: Learn how to manipulate Numpy arrays ( http://www.engr.ucsb.edu/~shell/che210d/numpy.pdf ) and how to read and manipulate data with Pandas ( https://www.youtube.com/watch?v=p8hle-ni-DM ).
3: Do the Kaggle Titanic survival prediction challenge with Random Forests. ( https://www.kaggle.com/c/titanic/details/getting-started-wit... )
4: Study Scikit-learn documentation ( http://scikit-learn.org/stable/documentation.html ). Run a few examples. Change RandomForestClassifier into SGDClassifier and play with the results. Scale the data to make it perform better. Combine a RF model and a SGD model through averaging
Read the rest
and try to improve the benchmark score.
5: Study the ensemble module of Scikit-learn. Try the examples on the wiki of XGBoost ( https://github.com/dmlc/xgboost/tree/master/demo/binary_clas... ) and Vowpal Wabbit ( http://zinkov.com/posts/2013-08-13-vowpal-tutorial/ ). Practically you want to get to a stage of: Getting the data transformed to be accepted by the algo, a form of evaluation, and then getting the predictions back out in a sensible form.
Then next week start competing on Kaggle and form a team to join up with people at your level. You will learn a lot that way and start to open up the black box.
I found these series very accessible: http://blog.kaggle.com/2015/04/22/scikit-learn-video-3-machi...
Kaggle also recently released a feature to run machine learning scripts in your browser. You could check those out and check out Python, R, common pipelines and even the more advanced neural nets: https://www.kaggle.com/users/9028/danb/digit-recognizer/big-... .