  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.
