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This paper describes a generic framework for explaining the prediction of probabilistic machine learning
algorithms using cases. The framework consists of two components: a similarity metric between cases that
is defined relative to a probability model and an novel casebased approach to justifying the probabilistic
prediction by estimating the prediction error using casebased reasoning. As basis for deriving similarity
metrics, we define similarity in terms of the principle of interchangeability that two cases are considered
similar or identical if two probability distributions, derived from excluding either one or the other case in the
case base, are identical. Lastly, we show the applicability of the proposed approach by deriving a metric for
linear regression, and apply the proposed approach for explaining predictions of the energy performance of
households.
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