Lime is short for Local Interpretable Model Agnostic Explanations Each part of the name reflects something that we desire in explanations Local refers to local fidelity we want the explanation to really reflect the behaviour of the classifier around the instance being predicted
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Get Pricefrom import LimeTextExplainer explainer = LimeTextExplainer class names = class names Previously we used the default parameter for label when generating explanation which works well in the binary case For the multiclass case we have to determine for which labels we will get explanations via the labels parameter
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Get PriceOnce the class probabilities for each variation is returned this can be fed to the LimeTextExplainer class shown below Enabling bag of words bow would mean that LIME doesn t consider word order when generating the FastText and Flair models were trained considering n grams and contextual ordering respectively so for a fair comparison between models the bow flag
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Get PriceWhile treating the model as a black box LIME perturbs the instance desired to explain and learn a sparse linear model around it as an explanation The figure below illustrates the intuition for this procedure The model s decision function is represented by the blue/pink background and is clearly nonlinear
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