Finally, the world changes, and models don’t. Implicit in the ML process of data set construction, model training, and model evaluation is the assumption that the future will be the same as the past. This assumption is known as the stationarity assumption: the processes or behaviors that are being modeled are stationary through time (i.e., they don’t change). Data sets are intrinsically historic in the sense that data are representations of observations that were made in the past. So, in effect, ML algorithms search through the past for patterns that might generalize to the future. Obviously,
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