Predictive and interpretable models via the stacked elastic net

Motivation: Machine learning in the biomedical sciences should ideally provide predictive and interpretable models. When predicting outcomes from clinical or molecular features, applied researchers often want to know which features have effects, whether these effects are positive or negative and how strong these effects are. Regression analysis includes this information in the coefficients but typically renders less predictive models than more advanced machine learning techniques. Results: Here, we propose an interpretable meta-learning approach for high-dimensional regression. The elastic net provides a compromise between estimating weak effects for many features and strong effects for some features. It has a mixing parameter to weight between ridge and lasso regularization. Instead of selecting one weighting by tuning, we combine multiple weightings by stacking. We do this in a way that increases predictivity without sacrificing interpretability.

Automated summary

The study introduced an interpretable meta-learning approach for high-dimensional regression, called the elastic net, which balances weak effects for many features and strong effects for some features. By combining multiple weightings through stacking, it enhances predictive performance while maintaining interpretability.