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