I regularly deal with data in which I have a single metric that is computationally expensive to calculate. I also have numerous (less than a dozen) low-resolution metrics that attempt to approximate different elements of the expensive metric. I'd like to take a relatively small training set for which I have both the expensive and inexpensive metrics (say, 1000 or so instances) and come up with a model that predicts the high-resolution data from the low. All of my metrics are real numbers on a continuous scale. It is likely that some of the inexpensive metrics will be correlated.
I'm completely ignorant to virtually all machine learning techniques, but assumed this would be a common enough scenario that there would be some "canned" way of doing this (my naive hope was a simple online tool would exist). In the absence of that, what would be a straightforward way of accomplishing my goal?