What Are Local Interpretable Model-Agnostic Explanations -- or What is LIME -- In Machine Learning?
LIME, or “Local Interpretable Model-Agnostic Explanations,” is an explainability method that attempts to provide local ML explainability. At a high level, LIME attempts to understand how perturbations in a model’s inputs affect the end-prediction of the model. Since it makes no assumptions about how the model reaches the prediction, it can be used with any model architecture, hence the “model-agnostic” part of LIME. The LIME explainability approach takes a single input value of predictions and perturbs the inputs around those values. It then builds a linear model off of the feature perturbations where the coefficients are the feature importances at this local prediction.