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Context-Aware Automated Feature Engineering (CAAFE) is an automated machine learning tool that uses large language models for feature engineering in tabular datasets. It generates Python code for new features along with explanations for their utility, enhancing interpretability.

Project description

Usage

Use this colab notebook for a quickstart.

Use CAFE_minimal.ipynb for a minimal example of how to use CAAFE on your dataset.

Use CAAFE.ipynb to reproduce the experiments from the paper.

Choosing an Iterative Classifier

The iterative classifier gets called in each

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OpenAI

Paper

Hollmann, N., Müller, S., & Hutter, F. (2023). LLMs for Semi-Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering https://arxiv.org/abs/2305.03403

License

CC BY-NC-SA 4.0

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

CC BY-NC-SA 4.0

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