RERexxar·Mar 6, 2024
Is it this library https://github.com/google/yggdrasil-decision-forests ?
In Why do tree-based models still outperform deep learning on tabular data? (2022)google
yggdrasil-decision-forests
C++2022–2024
google / yggdrasil-decision-forests
yggdrasil-decision-forests
A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
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2Mentions
0Explicit recommenders
680GitHub stars
What to know before exploring
What it is
A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
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Where it is used
No documented product use has been verified yet.
What people are saying
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Also in these discussions
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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, 5:04:57 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 month2 dated mentions
Observed mentions: 2022-08-04 08:21:52 UTC to 2024-03-06 07:56:27 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 |
|---|---|---|---|---|---|
| Aug 2022 | 2022-08-01 00:00:00 UTC | 2022-09-01 00:00:00 UTC | 1 | 0 | 1 |
| Sep 2022 | 2022-09-01 00:00:00 UTC | 2022-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2022 | 2022-10-01 00:00:00 UTC | 2022-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2022 | 2022-11-01 00:00:00 UTC | 2022-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2022 | 2022-12-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2023 | 2023-01-01 00:00:00 UTC | 2023-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2023 | 2023-02-01 00:00:00 UTC | 2023-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2023 | 2023-03-01 00:00:00 UTC | 2023-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2023 | 2023-04-01 00:00:00 UTC | 2023-05-01 00:00:00 UTC | 0 | 0 | 0 |
| May 2023 | 2023-05-01 00:00:00 UTC | 2023-06-01 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2023 | 2023-06-01 00:00:00 UTC | 2023-07-01 00:00:00 UTC | 0 | 0 | 0 |
| Jul 2023 | 2023-07-01 00:00:00 UTC | 2023-08-01 00:00:00 UTC | 0 | 0 | 0 |
| Aug 2023 | 2023-08-01 00:00:00 UTC | 2023-09-01 00:00:00 UTC | 0 | 0 | 0 |
| Sep 2023 | 2023-09-01 00:00:00 UTC | 2023-10-01 00:00:00 UTC | 0 | 0 | 0 |
| Oct 2023 | 2023-10-01 00:00:00 UTC | 2023-11-01 00:00:00 UTC | 0 | 0 | 0 |
| Nov 2023 | 2023-11-01 00:00:00 UTC | 2023-12-01 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2023 | 2023-12-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 0 | 0 | 0 |
| Jan 2024 | 2024-01-01 00:00:00 UTC | 2024-02-01 00:00:00 UTC | 0 | 0 | 0 |
| Feb 2024 | 2024-02-01 00:00:00 UTC | 2024-03-01 00:00:00 UTC | 0 | 0 | 0 |
| Mar 2024 | 2024-03-01 00:00:00 UTC | 2024-04-01 00:00:00 UTC | 1 | 0 | 1 |
What Hacker News says
2 sourcesI can't explain it, but I help maintain TensorFlow Decision Forests [1] and Yggdrasil Decision Forests [2], and in an AutoML system at work that trains models on lots of various users data, decision forest models gets selected as best (after AutoML tries various model types and hyperparameters) somewhere between 20% to 40% of the times, systematically. It's pretty interesting. Other ML types considered are NN, linear models (with auto feature crossings generation), and a couple of other variations.
[1] https://github.com/tensorflow/decision-forests
[2] https://github.com/google/yggdrasil-decision-forests
In Why do tree-based models still outperform deep learning on tabular data?