Machines are increasingly doing “intelligent” things: Facebook recognizes faces in photos, Siri understands voices, and Google translates websites. The fundamental insight behind these breakthroughs is as much statis- tical as computational. Face recognition algorithms, for example, use a large dataset of photos labeled as having a face or not to estimate a function f(x) that predicts the presence y of a face from pixels x. This similarity to econometrics raises questions: How do these new empirical tools fit with what we know? As empirical economists, how can we use them? We present a way of thinking about machine learning that clarifies its place in the econometric toolbox. Machine learning not only provides new tools, it solves a specific problem. Machine learning revolves around prediction on new sample points from the same distribution, while many economic applications revolve around parameter estimation and counterfactual prognosis. So applying machine learning to economics requires finding relevant prediction tasks.