Abstract
We consider the modelling of parametrized processes, where the goal is to model the process for new parameter value combinations. We compare the classical regression approach to a modular approach based on regression in the model space: First, for each process parametrization a model is learned. Second, a mapping from process parameters to model parameters is learned. We evaluate both approaches on two synthetic and two real-world data sets and show the advantages of the regression in the model space.