Local Interpretable Model-Agnostic Explanations

Local Interpretable Model-Agnostic Explanations (LIME) is an ML explainability technique. It is a model-agnostic method, and it is local in that it only explains the prediction for a single instance. LIME works by perturbing the input data and observing how this affects the output of the model. For each perturbation, LIME calculates an importance score, which represents how important that feature was in determining the predicted outcome. The final explanation is generated by taking a weighted sum of all these importance scores.

LIME works by approximating the decision boundary around the instance to be explained (the so-called local model), and then computing how changes to input values would affect predictions made by this model (the so-called perturbation). This approach allows us to understand which input features are most important in determining the predicted output, without having to rely on global assumptions about feature importance...