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It first generates a wide range of non-linear features, then selects a small, robust subset of meaningful features that enhance the predictive power of linear models. This multi-step approach allows you to harness the interpretability of linear models without sacrificing accuracy.\n\n### Key Features:\n- **Automated Feature Generation and Selection**: Automates the process of generating and selecting features for linear models for improved performance.\n- **Improved Performance and Interpretability**: The generated features improve prediction accuracy while retaining the intuitive interpretability of linear models.\n- **Seamless Integration**: Fully compatible with `scikit-learn` pipelines, making it easy to integrate into your existing machine learning workflows.\n\n### Use Cases:\n- Ideal for **supervised learning tasks** where model transparency is crucial for decision-making.\n- Suitable for **feature selection** in large datasets, automating the discovery of important variables.\n- Useful in scenarios where **non-linear features** need to be discovered and leveraged without complicating the model.\n\n**Note:** The code is intended for research purposes. Results may vary depending on the dataset and use case.\n\n## Installation\n\nAutofeat is available on PyPI, making it easy to install via `pip`:\n\n```\npip install autofeat\n```\n### Other Dependencies\n- numpy\n- pandas\n- scikit-learn\n- sympy\n- joblib\n- pint\n- numba\n\n## Documentation and Resources\n| Description | Link |\n|-------------|------|\n| Example Notebooks | [examples](/notebooks/) |\n| Documentation | [documentation](https://franziskahorn.de/autofeat) |\n| Paper | [paper](https://arxiv.org/abs/1901.07329) |\n| Talk | [PyData talk](https://www.youtube.com/watch?v=4-4pKPv9lJ4) |\n\nIf any of this code was helpful for your work, please consider citing the paper:\n```\n@inproceedings{horn2019autofeat,\n  title={The autofeat Python Library for Automated Feature Engineering and Selection},\n  author={Horn, Franziska and Pack, Robert and Rieger, Michael},\n  booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases},\n  pages={111--120},\n  year={2019},\n  organization={Springer}\n}\n```\n\nIf you have any questions please don't hesitate to send me an [email](mailto:cod3licious@gmail.com) and of course if you should find any bugs or want to contribute other improvements, pull requests are very welcome!\n\n## Acknowledgments\nThis project was made possible thanks to support by [BASF](https://www.basf.com).\n\n","funding_links":[],"categories":["Libraries"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcod3licious%2Fautofeat","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcod3licious%2Fautofeat","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcod3licious%2Fautofeat/lists"}