Local Interpretable Model-Agnostic Explanations (LIME)

LIME explains a model's prediction by creating variations of a specific input and observing how the model's output changes. It then uses these examples to build a simpler local model, such as a linear model, that approximates the original model's behavior near that input. This helps identify which features contributed most to the prediction.

LIME makes complex machine learning models easier to understand without requiring access to their internal architecture. By showing which features influenced individual predictions, it can help teams analyze model behavior, identify potential biases, validate decisions, and improve trust in AI systems.