Summary of Autokaggle: a Multi-agent Framework For Autonomous Data Science Competitions, by Ziming Li et al.
AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions
by Ziming Li, Qianbo Zang, David Ma, Jiawei Guo, Tuney Zheng, Minghao Liu, Xinyao Niu, Yue Wang, Jian Yang, Jiaheng Liu, Wanjun Zhong, Wangchunshu Zhou, Wenhao Huang, Ge Zhang
First submitted to arxiv on: 27 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed AutoKaggle framework is a collaborative multi-agent system designed to assist data scientists in completing daily data pipelines. This user-centric approach combines code execution, debugging, and unit testing to ensure correct logic and consistency. The framework offers customizable workflows, allowing users to intervene at each phase and integrate automated intelligence with human expertise. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AutoKaggle is a powerful tool that helps data scientists complete complex tasks. It’s like having an assistant who can help you clean your data, create new features, and build models. This system uses a special process to make sure the code works correctly and is easy to understand. You can even customize how it works so you can work alongside the automated parts. |
