In the analysis of big data sets, the first step is usually the identification of “features”—data points with particular predictive power or analytic utility. Choosing features usually requires some human intuition. For instance, a sales database might contain revenues and date ranges, but it might take a human to recognize that average revenues—revenues divided by the sizes of the ranges—is the really useful metric.