Knowledge-Guided Additive Modeling For Supervised Regression
Yann Claes Vân Anh Huynh-Thu Pierre Geurts
Learning processes by exploiting restricted domain knowledge is an important
task across a plethora of scientific areas, with more and more hybrid methods
combining data-driven and model-based approaches. However, while such hybrid
methods have been tested in various scientific applications, they have been
mostly tested on dynamical systems, with only limited study about the influence
of each model component on global performance and parameter identification. In
this work, we assess the performance of hybrid modeling against traditional
machine learning methods on standard regression problems. We compare, on both
synthetic and real regression problems, several approaches for training such
hybrid models. We focus on hybrid methods that additively combine a parametric
physical term with a machine learning term and investigate model-agnostic
training procedures. We also introduce a new hybrid approach based on partial
dependence functions. Experiments are carried out with different types of
machine learning models, including tree-based models and artificial neural
networks.