# The autofeat Python Library for Automated Feature Engineering and Selection

## TL;DR

The autofeat Python library automates feature engineering and selection to enhance linear models' accuracy while maintaining interpretability, bridging the gap between simple and complex machine learning models.

## Contribution

This paper introduces autofeat, a library that automates feature engineering and selection for linear models, improving their predictive performance without sacrificing transparency.

## Key findings

- Enhanced prediction accuracy of linear models.
- Automated multi-step feature engineering process.
- Maintained interpretability of models.

## Abstract

This paper describes the autofeat Python library, which provides scikit-learn style linear regression and classification models with automated feature engineering and selection capabilities. Complex non-linear machine learning models, such as neural networks, are in practice often difficult to train and even harder to explain to non-statisticians, who require transparent analysis results as a basis for important business decisions. While linear models are efficient and intuitive, they generally provide lower prediction accuracies. Our library provides a multi-step feature engineering and selection process, where first a large pool of non-linear features is generated, from which then a small and robust set of meaningful features is selected, which improve the prediction accuracy of a linear model while retaining its interpretability.