  Feature selection is an essential step in data science pipelines to reduce
the complexity associated with large datasets. While much research on this
topic focuses on optimizing predictive performance, few studies investigate
stability in the context of the feature selection process. In this study, we
present the Repeated Elastic Net Technique (RENT) for Feature Selection. RENT
uses an ensemble of generalized linear models with elastic net regularization,
each trained on distinct subsets of the training data. The feature selection is
based on three criteria evaluating the weight distributions of features across
all elementary models. This fact leads to the selection of features with high
stability that improve the robustness of the final model. Furthermore, unlike
established feature selectors, RENT provides valuable information for model
interpretation concerning the identification of objects in the data that are
difficult to predict during training. In our experiments, we benchmark RENT
against six established feature selectors on eight multivariate datasets for
binary classification and regression. In the experimental comparison, RENT
shows a well-balanced trade-off between predictive performance and stability.
Finally, we underline the additional interpretational value of RENT with an
exploratory post-hoc analysis of a healthcare dataset.
