seminars
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Explaining Model Behavior Across Space and Time: Differential and Intertemporal Explanations
Abstract: We introduce the notion of differential explanation as information provided to users of machine learning models that explains why a model assigns different values to two observations. We propose a model-agnostic feature importance method based on SHapley Additive exPlanations (Lundberg and Lee 2017), to generate such explanations that can be viewed as its generalization.… Continue Reading Explaining Model Behavior Across Space and Time: Differential and Intertemporal Explanations
