  The need to approximate functions is ubiquitous in science, either due to
empirical constraints or high computational cost of accessing the function. In
high-energy physics, the precise computation of the scattering cross-section of
a process requires the evaluation of computationally intensive integrals. A
wide variety of methods in machine learning have been used to tackle this
problem, but often the motivation of using one method over another is lacking.
Comparing these methods is typically highly dependent on the problem at hand,
so we specify to the case where we can evaluate the function a large number of
times, after which quick and accurate evaluation can take place. We consider
four interpolation and three machine learning techniques and compare their
performance on three toy functions, the four-point scalar Passarino-Veltman
$D_0$ function, and the two-loop self-energy master integral $M$. We find that
in low dimensions ($d = 3$), traditional interpolation techniques like the
Radial Basis Function perform very well, but in higher dimensions ($d=5, 6, 9$)
we find that multi-layer perceptrons (a.k.a neural networks) do not suffer as
much from the curse of dimensionality and provide the fastest and most accurate
predictions.
