In this paper, Breiman characterizes the traditional approach to statistics as a data-modeling culture that views the primary goal of data analysis as identifying the (hidden) stochastic data model (e.g., linear regression) that explains how the data were generated. He contrasts this culture with the algorithmic-modeling culture that focuses on using computer algorithms to create prediction models that are accurate (rather than explanatory in terms of how the data was generated). Breiman’s distinction between a statistical focus on models that explain the data versus an algorithmic focus on
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