Local Interpretable Model-Agnostic Explanations (LIME)

LIME, or Local Interpretable Model-Agnostic Explanations, is an algorithm that can explain the predictions of any classifier or regressor in a faithful way, by approximating it locally with an interpretable model. It modifies a single data sample by tweaking the feature values and observes the resulting impact on the output. It performs the role of an 'explainer' to explain predictions from each data sample. The output of LIME is a set of explanations representing the contribution of each feature to a prediction for a single sample, which is a form of local interpretability.Interpretable models in LIME can be, for instance, linear regression or decision trees, which are trained on small perturbations (e.g. adding noise, removing words, hiding parts of the image) of the original model to provide a good local approximation.
相关学科: SHAPExplainable Artificial IntelligenceFeature ImportanceInterpretabilityInterpretable Machine LearningLow-Light Image EnhancementCounterfactual ExplanationSuperpixelsLink DiscoveryText Classification









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