  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.
