Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A Living Benchmark for Machine Learning on Tabular Data
| Date | Stars |
|---|---|
| 2026-07-31 | 276 |
| 2026-08-06 | 281 |
Today
+5 stars today
This week
— stars this week
This month
— stars this month
Momentum
20.0
growth rate 0.00%/day
<div align="center">
<div id="user-content-toc">
<ul align="center" style="list-style: none;">
<summary>
<img src="https://avatars.githubusercontent.com/u/210855230" width="175" alt="TabArena Logo"/>
</summary>
</ul>
</div>
## A Living Benchmark for Machine Learning on Tabular Data 💫
---
| 🚀 [Leaderboard](https://tabarena.ai/) | 📂 [Example Scripts]( https://tabarena.ai/code-examples) | 📊 [Dataset Curation](https://tabarena.github.io/data-foundry/) | 📄 Papers: [TabArena-v0.1](https://arxiv.org/abs/2506.16791) · [BeyondArena](https://arxiv.org/abs/2606.30410) |
|:--------------------------------------:|:----------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|
---
</div>
TabArena is a living benchmarking system that makes benchmarking tabular machine learning models a reliable experience. TabArena implements best practices to ensure methods are represented at their peak potential, including cross-validated ensembles, strong hyperparameter search spaces contributed by the method authors, early stopping, model refitting, parallel bagging, memory usage estimation, and more. Explore the latest results on the [live leaderboard](https://huggingface.co/spaces/TabArena/leaderboard).
This single codebase powers **two complementary benchmarks** that share the same fitting, runner, and
evaluation code:
- 🏟️ **TabArena-v0.1** — the living benchmark on **curated, IID** tabular datasets.
- 🌍 **BeyondArena** — a holistic, ***beyond-IID*** benchmark spanning **IID, temporal, and grouped**
tasks across a wide range of dataset sizes and feature dimensionalities.
**BeyondArena will superseed TabArena-v0.1 in the future.**
> [!TIP]
> **New here? Start with TabArena, then graduate to BeyondArena.** Get your model working and
> competitive on TabArena's curated IID datasets first; once it holds up there, run the *same* code
> on BeyondArena to stress-test how well it generalizes beyond IID.
**TabArena** covers 51 curated datasets (9–30 splits each) and 27+ methods, including 10+ tabular
foundation models — over 50M trained models, with all validation and test predictions cached for
tuning and post-hoc ensembling. **BeyondArena** extends this to **[142 datasets](https://huggingface.co/datasets/TabArena/BeyondArena)** across IID,
temporal, and grouped task types, spanning tiny to 1M-row datasets and low- to high-dimensional
features.
## ⚡ Quickstart
> [!TIP]
> The fastest way to try TabArena end-to-end:
```bash
pip install uv
git clone https://github.com/autogluon/tabarena.git && cd tabarena
uv venv --seed --python 3.12 && source .venv/bin/activate
uv pip install --prerelease=allow -e "./packages/tabarena[benchmark]"
python examples/benchmarking/run_quickstart_tabarena.py
```
For other install paths (eval-only, editable AutoGluon, dependency), see [Installation](#-installation) below.
To try **BeyondArena** instead, run `python examples/beyondarena/run_quickstart_beyondarena.py` with the same install.
## 🕹️ Use Cases
We share more details on various use cases of TabArena in our [examples](examples):
* 🌍 **Benchmarking Beyond IID (BeyondArena)**: please refer to [examples/beyondarena](examples/beyondarena).
* 📊 **Benchmarking Predictive Machine Learning Models**: please refer to [examples/benchmarking](examples/benchmarking).
* 🚀 **Using SOTA Tabular Models Benchmarked by TabArena**: please refer to [examples/running_tabarena_models](examples/running_tabarena_models).
* 🧪 **Advanced and Specialized Usage**: please refer to [examples/advanced](examples/advanced).
* 🗃️ **Analysing Metadata and Meta-Learning**: please refer to [examples/meta](examples/meta).
* 📈 **Generating Plots and Leaderboards**: please refer to [examples/plots](examples/plots).
* 🔁 **ReproducibilExcerpt of 11,480 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:228323c4728b6512, llm:Repository description: 'A Living Benchmark for Machine Learning on Tabular Data' (AutoGluon project).
matched fp:228323c4728b6512, llm:Repository description: 'A Living Benchmark for Machine Learning on Tabular Data' (AutoGluon project).
matched fp:228323c4728b6512, llm:Repository description: 'A Living Benchmark for Machine Learning on Tabular Data' (AutoGluon project).