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https://www.doi.org/10.1007/978-3-319-91473-2_9

Comparison-Based Inverse Classification for Interpretability in Machine Learning

In the context of post-hoc interpretability, this paper addresses the task of explaining the prediction of a classifier, considering the case where no information is available, neither on the classifier itself, nor on the processed data (neither the training nor the...



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Comparison-Based Inverse Classification for Interpretability in Machine Learning

https://www.doi.org/10.1007/978-3-319-91473-2_9

In the context of post-hoc interpretability, this paper addresses the task of explaining the prediction of a classifier, considering the case where no information is available, neither on the classifier itself, nor on the processed data (neither the training nor the...



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https://www.doi.org/10.1007/978-3-319-91473-2_9

Comparison-Based Inverse Classification for Interpretability in Machine Learning

In the context of post-hoc interpretability, this paper addresses the task of explaining the prediction of a classifier, considering the case where no information is available, neither on the classifier itself, nor on the processed data (neither the training nor the...

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