# Advice on machine learning for small inputs and outputs

I am planning on using a machine learning algorithm to learn the mapping between sets of four coordinates (x,y,z + a distance d from a reference point) to two numbers (an amplitude A and a time t). In other words, a machine learning algorithm should learn, for each sample i, the mapping

(x[i], y[i], z[i], d[i]) --> (A[i], t[i])


The coordinates x,y,z are integer numbers (because they are actually grid points on a fixed grid). The distance d is a decimal number instead.

The amplitude A is also a decimal number, while t is integer (because again, it represents a shift on a time grid).

What would be the best machine learning technique to use in this case? I thought of Gaussian process, maybe a neural network (if so, which type?)

If that matters, the sizes of my trianing and testing samples are 1500 and 500, respectively.