LIME (Local Interpretable Model-agnostic Explanations) explains individual predictions using local linear approximations. Approach: Create perturbed samples around the instance to explain, get model predictions on perturbations, fit interpretable model (linear) locally, use local model's features as explanation. For text: Remove words to create perturbations, predict on each variant, fit sparse linear model to identify important words. Algorithm: Sample neighborhood → weight by proximity to original → fit weighted linear model → extract top features. Output: List of features with positive/negative contributions to prediction. Advantages: Model-agnostic (works on any classifier), interpretable output, local fidelity to complex model. Limitations: Instability (different runs give different explanations), neighborhood definition affects results, doesn't explain global model behavior. Comparison to SHAP: LIME is local approximation, SHAP uses Shapley values. SHAP often more stable but more expensive. Tools: lime library (Python), supports text, tabular, image. Use cases: Debug classification errors, understand individual predictions, build user trust. Foundational explainability method.