In higher dimensions, you could do a series of 1d regressions, something like this: 1. Regress x1 against y. Call your curve f1(x1). 2. Regress x2 against (y

In higher dimensions, you could do a series of 1d regressions, something like this: 1. Regress x1 against y. Call your curve f1(x1). 2. Regress x2 against (y - f1(x1)). Call your curve f2(x2). 3. Regress x3 against (y - f1(x1) - f2(x2)). Call your curve f3(x3). 4. etc. You'd have to use your computer to read the curves and recompute the residuals each iteration, but there still isn't any algorithmic machine learning. I suspect this would work pretty well, but it would struggle when there are strong interactions between the different input variables.