Optimally Weighted Ensembles of Regression Models: Exact Weight Optimization and Applications
This work addresses the challenge of improving regression accuracy for practitioners by offering a more efficient and exact method for ensemble weighting, though it is incremental as it builds on prior heuristic optimization.
The paper tackles the problem of automated model selection in regression tasks by proposing optimally weighted ensembles of heterogeneous regression models, showing that this approach outperforms selecting a single best model on various datasets, including drug discovery data with mixed variable types.
Automated model selection is often proposed to users to choose which machine learning model (or method) to apply to a given regression task. In this paper, we show that combining different regression models can yield better results than selecting a single ('best') regression model, and outline an efficient method that obtains optimally weighted convex linear combination from a heterogeneous set of regression models. More specifically, in this paper, a heuristic weight optimization, used in a preceding conference paper, is replaced by an exact optimization algorithm using convex quadratic programming. We prove convexity of the quadratic programming formulation for the straightforward formulation and for a formulation with weighted data points. The novel weight optimization is not only (more) exact but also more efficient. The methods we develop in this paper are implemented and made available via github-open source. They can be executed on commonly available hardware and offer a transparent and easy to interpret interface. The results indicate that the approach outperforms model selection methods on a range of data sets, including data sets with mixed variable type from drug discovery applications.