Can't say.
I just used Neural because it is a flexible interpolator. It served my purpose. I did not mean to imply that it is the BEST interpolator in this case or any other case in general. I got my benefit. I did not consider any other interpolator. Another one might have done better in this case, but this result was 'gut genug.'
The choice of the best model ALWAYS DEPENDS. It isn't 'plug and chug.'
Many niches of predictive modeling have their favorite model and never look at other models. Maybe it was the best model the first time that a model was trained. Well, data changes and so should the predictive model. The characteristics of the data determine the choice of the best type of model type but the characteristics of the data change over time. So we need to refresh the model as the data changes and probably need to re-evaluate the choice of the type of model, too. (We won't get into ensemble models today.)
A reply to a question in this discussion is incapable of explaining all the pros and cons of all the types of predictive models in different situations. No type of model is superior. I suggest reading "Elements of Statistical Learning."
