LIME (Local Interpretable Model-agnostic Explanations)

LIME (Local Interpretable Model-agnostic Explanations) is a technique that helps us understand how complex machine learning models make decisions. Instead of trying to interpret the entire model, LIME focuses on explaining individual predictions by creating a simple, understandable model—like a small decision tree—that approximates the complex model’s behavior for that specific case. It does this by tweaking the input slightly many times and observing how the output changes. This way, LIME provides clear insights into which features influenced a particular prediction, making opaque models more transparent and trustworthy.