LIME (Local Interpretable Model-agnostic Explanations) is a technique that explains individual predictions of any machine learning model by approximating its behavior locally with a simpler, interpretable model. It works by generating variations of the input data, observing how the original model responds to these variations, and then training a transparent surrogate model (like linear regression) on this perturbed dataset. The surrogate model’s coefficients highlight which input features most influenced the prediction for the specific instance being explained.
The process starts by creating perturbed versions of the input instance. For example, in text classification, this might involve removing or shuffling words; for images, it could mean masking parts of the image. Each perturbed sample is passed through the original model to get a prediction. These samples are weighted based on their proximity to the original instance—closer samples have more influence. Next, a simple model (e.g., a sparse linear model) is trained on this dataset to mimic the original model’s predictions in the local region. The coefficients of this surrogate model indicate feature importance, showing which inputs (like specific words or pixels) the original model relied on for its decision.
Consider a scenario where a neural network classifies a movie review as positive. LIME might generate perturbed reviews by removing words like “excellent” or “boring” and observe how the prediction changes. If removing “excellent” causes the predicted probability of “positive” to drop significantly, LIME highlights that word as influential. However, LIME’s effectiveness depends on parameters like the number of perturbed samples and the distance metric used. While it provides intuitive insights, the explanations are approximations and may vary slightly between runs. Developers should use LIME to validate model behavior case-by-case, balancing interpretability with the fidelity of the surrogate model to the original.