Across diverse fields, investigators face problems and opportunities involving data. Scientists, scholars, engineers, and other analysts seek new methods to ingest data, extract salient patterns, and then use the results for prediction and understanding. These methods come from machine learning (ML), which is quickly becoming core to modern technological systems, modern scientific workflow, and modern approaches to understanding data.
The classical approach to solving a problem with ML follows the "cookbook" approach, one where the scientist shoehorns her data and problem to match the inputs and outputs of a reliable ML method. This strategy has been successful in many domains—examples include spam filtering, speech recognition, and movie recommendation—but it can only take us so far. The cookbook focuses on prediction at the expense of explanation, and thus values generic and flexible methods. In contrast, many modern ML applications require interpretable methods that both form good predictions and suggest good reasons for them. Further, as data becomes more complex and ML problems become more varied, it becomes more difficult to shoehorn our diverse problems into a simple ML set-up.
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