Learning-From-Disagreement: A Model Comparison and Visual Analytics Framework. (arXiv:2201.07849v1 [cs.LG])

With the fast-growing number of classification models being produced every
day, numerous model interpretation and comparison solutions have also been
introduced. For example, LIME and SHAP can interpret what input features
contribute more to a classifier's output predictions. Different numerical
metrics (e.g., accuracy) can be used to easily compare two classifiers.
However, few works can interpret the contribution of a data feature to a
classifier in comparison with its contribution to another classifier. This
comparative interpretation can help to disclose …

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