Model Class Reliance: Variable Importance Measures for any Machine Learning Model Class, from the "Rashomon" Perspective

4 Jan 2018 Aaron Fisher Cynthia Rudin Francesca Dominici

Variable importance (VI) tools are typically used to examine the inner workings of prediction models. However, many existing VI measures are not comparable across model types, can obscure implicit assumptions about the data generating distribution, or can give seemingly incoherent results when multiple prediction models fit the data well... (read more)

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