LIME explains a prediction of a machine learning model (classification or regression) for a query point by finding important predictors and fitting a simple interpretable model. For regression, this isn’t quite as interesting (although it is useful). The interpretable inputs map t… Let p be the dimension of the original feature space X and let p’ be the dimension of the interpretable space X’. To ensure that the explanation is interpretable, LIME distinguishes an interpretable representationfrom the original feature space that the model uses. As an example, if you are trying to classify plants as edible or poisonous, LIME’s explanation is much more useful. The LIME approach shows much more benefit (at least to me) when performing classification. Here’s an example from the authors. The interpretable representation has to be understandable to humans, so its dimension is not necessarily the same as the dimension of the original feature space.

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