  Gravitational-wave astronomy of compact binaries relies on theoretical models
of the gravitational-wave signal that is emitted as binaries coalesce. These
models do not only need to be accurate, they also have to be fast to evaluate
in order to be able to compare millions of signals in near real time with the
data of gravitational-wave instruments. A variety of regression and
interpolation techniques have been employed to build efficient waveform models,
but no study has systematically compared the performance of these regression
methods yet. Here we provide such a comparison of various techniques, including
polynomial fits, radial basis functions, Gaussian process regression and
artificial neural networks, specifically for the case of gravitational waveform
modeling. We use all these techniques to regress analytical models of
non-precessing and precessing binary black hole waveforms, and compare the
accuracy as well as computational speed. We find that most regression methods
are reasonably accurate, but efficiency considerations favour in many cases the
most simple approach. We conclude that sophisticated regression methods are not
necessarily needed in standard gravitational-wave modeling applications,
although problems with higher complexity than what is tested here might be more
suitable for machine-learning techniques and more sophisticated methods may
have side benefits.
