The autofeat Python Library for Automated Feature Engineering and Selection

2019/01/22 by Franziska Horn, Robert Pack, Horn, Franziska +5 · 23 citations
Computer Science · Mathematics · #Anomaly Detection Techniques and Applications #Machine Learning and Data Classification #Neural Networks and Applications #cs.LG #stat.ML

paper · pdf · doi:10.48550/arxiv.1901.07329

ECMLPKDD 2019 Workshop on Automating Data Science (ADS)

arxiv created 2020/02/26 · arxiv updated 2020/02/27

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.

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• Evaluation of the impact of expert knowledge: How decision support scores impact the effectiveness of automatic knowledge-driven feature engineering (aKDFE)

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