LIME Model

The LIME (Interpretable Model-Agnostic Explanations) model is a powerful technique designed to discover complex machine learning model predictions. Operating on the principle of local interpretation, LIME generates intuitive explanations for specific predictions by approximating the behavior of the observed black-box model. This is achieved by making clear examples and making simple models, such as local linear regression. This process involves dividing the original image into superpixels, generating noise through binary vectors, creating a local database, and preparing a surrogate model. By analyzing the surrogate model coefficients, LIME identifies important features or superpixels that influence the prediction and provides important insights into the decision-making process of the black-box model. The step-by-step implementation of the code involves importing the necessary libraries, loading a ready-made model (InceptionV3), and displaying the LIME program on three different images (ships, planes, and cars) to explain advanced predictions and important features that contribute to those predictions.