Data Strategy

October 20, 2007

Statistical models beating experts

Filed under: Datamining, Pattern recognition — chucklam @ 9:55 pm

Ian Ayres published an edited excerpt from his book ‘Super Crunchers: How Anything Can Be Predicted’ in the Financial Times back in August. The piece revolves around the idea that “Since the 1950s, social scientists have been comparing the predictive accuracies of number crunchers and traditional experts – and finding that statistical models consistently outpredict experts.” This is hardly news to anyone who had studied pattern recognition. While statistical models are much worse than the average person on “simple” tasks (e.g. speech recognition), they generally outperform “experts” on “intelligent” tasks (e.g. medical diagnosis).

However, Ian still manages to cite examples that are interesting but not well known (at least to me). Six years ago, two political scientists, Andrew Martin and Kevin Quinn, developed a system that uses “just a few variables concerning the politics of the case” to predict how the US Supreme Court justices would vote. As a friendly contest, that system was pitted against a panel of 83 “legal experts – esteemed law professors, practitioners and pundits who would be called upon to predict the justices’ votes for cases in their areas of expertise.” The task “was to predict in advance the votes of the individual justices for every case that was argued in the Supreme Court’s 2002 term.” The statistical system won. It had a 75% accuracy versus 59.1% from the experts.

Another interesting example is that of Orley Ashenfelter, an economist at Princeton university. He devised a formula for predicting wine quality: Wine quality = 12.145 + 0.00117 winter rainfall + 0.0614 average growing season temperature – 0.00386 harvest rainfall. It’s not clear whether that equation outperforms experts. However, the equation seems good enough that it ruffled the feathers of quite a few wine snobs 🙂

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