"AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions"

The future is so multi-agentic.

In this paper, multi-agent system tackles complex data science tasks through collaborative problem-solving.

Phase-based workflow with debugging ensures robust data science automation

📚 https://arxiv.org/abs/2410.20424

🎯 Original Problem:

Data science tasks with tabular data require complex problem-solving approaches, but current LLM-based solutions focus on simple one-step analysis and lack interpretability in decision-making steps.

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🛠️ Solution in this Paper:

→ AutoKaggle: A multi-agent framework with 5 specialized agents (Reader, Planner, Developer, Reviewer, Summarizer)

→ Implements 6-phase workflow: background understanding, preliminary EDA, data cleaning, in-depth EDA, feature engineering, and model building

→ Uses iterative debugging with code execution, error correction, and unit testing (max 5 attempts per iteration)

→ Integrates comprehensive ML tools library for data cleaning, feature engineering, and modeling

→ Generates detailed reports after each phase to ensure transparency

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💡 Key Insights:

→ Phase-based workflow with specialized agents ensures systematic problem decomposition

→ Iterative debugging with unit testing prevents error propagation

→ Integration of predefined tools with self-generated code reduces reliance on LLMs

→ Detailed reporting enhances user trust and understanding

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📊 Results:

→ Evaluated on 8 Kaggle competitions

→ Achieved 0.85 validation submission rate

→ Scored 0.82 in comprehensive evaluation

→ Performed above average human level