DLIME uses agglomerative hierarchical clustering and KNN to generate stable local explanations for black-box ML predictions on medical data, outperforming LIME on Jaccard similarity of repeated explanations.
On The Stability of Interpretable Models
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abstract
Interpretable classification models are built with the purpose of providing a comprehensible description of the decision logic to an external oversight agent. When considered in isolation, a decision tree, a set of classification rules, or a linear model, are widely recognized as human-interpretable. However, such models are generated as part of a larger analytical process. Bias in data collection and preparation, or in model's construction may severely affect the accountability of the design process. We conduct an experimental study of the stability of interpretable models with respect to feature selection, instance selection, and model selection. Our conclusions should raise awareness and attention of the scientific community on the need of a stability impact assessment of interpretable models.
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cs.LG 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems
DLIME uses agglomerative hierarchical clustering and KNN to generate stable local explanations for black-box ML predictions on medical data, outperforming LIME on Jaccard similarity of repeated explanations.