As seen in the last part of Feature Engineering and polynomial regression LAB, we use a combination of powers of x to model a complex function. Is there an efficient way to know the best set of functions to accurately model the predicted data, or is it mostly by trial and error?
It’s mostly trial and error.
It’s said that a NN can learn an arbitrarily close approximation of any function, by using a suitable number and size of hidden layers.