Michael Rieger
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
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Michael Rieger has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Franziska Horn
Technische Universität Berlin Machine Learning Group Marchstr. 23 10587 Berlin Germany
1 shared publication - Robert Pack1 shared publication
Latest Publications
- Open Access
The autofeat Python Library for Automated Feature Engineering and Selection
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.
Open AccessThe autofeat Python Library for Automated Feature Engineering and Selection
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.