How LIME works | Understanding in 5 steps

Local Interpretable Model-agnostic Explanations (LIME) is a technique designed to enhance the interpretability of machine learning models by explaining the predictions made by black-box classifiers. The method balances the trade-off between model interpretability and predictive performance, which is crucial in fields where justifying model predictions is necessary. LIME operates by perturbing data samples and observing the changes in predictions made by the black-box model, then constructing an interpretable model based on these variations. This interpretable model, often a linear approximation, provides local explanations for individual predictions, as illustrated in a churn prediction example where LIME identified potential biases in the model's decision-making process. The technique is applicable to various data types, including tabular, text, and image data, and offers insights into feature importance, thus fostering trust in machine learning models. LIME represents a significant advancement in machine learning interpretability, alongside other techniques like SHAP, and remains an active area of research with ongoing exploration and development.