Trade-offs in Explanatory Model Learning
نویسنده
چکیده
In many practical applications, accuracy of a prediction is as important as understandability of the process that leads to it. Explanatory learning emerges as an important capability of systems designed for close interaction with human users. Many generic white-box predictive model types are readily available and potentially appropriate for the task (decision trees, association rules, sub-spacing, etc.) with more being developed each day. This project introduces an algorithm specifically designed to learn explainable models: Explanation Oriented Partitioning (EOP). Its performance is compared against a range of relevant alternatives using multiple synthetic and real-world data sets. EOP typically yields substantially simpler, more interpretable models, while maintaining comparable predictive accuracy.
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