LIME, or Local Interpretable Model-Agnostic Explanations, is a technique used to improve the interpretability of complex machine learning models. It focuses on explaining specific predictions made by any machine learning model, regardless of its underlying architecture. The idea behind LIME is to create a simpler, interpretable model that closely approximates the predictions of the complex model in the vicinity of a specific instance. By doing so, it provides insights into why the model made a particular decision based on the input data.
The process of LIME starts by selecting an instance for which we want to understand the model's output. LIME then generates a dataset of perturbed samples around that instance. This means it slightly modifies the input features and then collects the predictions from the original model for these new samples. For example, if the input is an image, small noise or alterations might be added to the pixels, or if the input is tabular data, various feature values could be changed. These new samples, along with their corresponding predictions, are then used to train a simpler, interpretable model, often a linear regression or a decision tree.
Finally, LIME provides explanations based on this simpler model, highlighting which features had the most significant impact on the prediction for the chosen instance. For instance, in a sentiment analysis model predicting a review's positivity, LIME might indicate that specific words like "excellent" or "disappointing" influenced the outcome. This explanation helps developers and stakeholders understand the model's behavior in a meaningful way, allowing them to build trust in automated systems and ensure compliance with decision-making processes.