What defines Glassbox Approaches in the context of machine learning?

ChatGPT 3.5 User What is Lime in XAI? ChatGPT In the context of Explainable Artificial Intelligence (XAI), Lime stands for "Local Interpretable Model-agnostic Explanations." It's a technique used to explain the predictions of machine learning models by approximating their decision boundaries locally around specific instances. Lime generates interpretable explanations by fitting simpler models, such as linear models, to perturbed versions of the original data points and observing how the predictions change. This helps users understand why a model made a particular prediction for a given instance. User How does this work? ChatGPT Lime works by creating perturbations or variations of the original input data and observing how these variations affect the model's predictions. Here's a simplified explanation of how it works: Select Instance: Choose the instance (data point) for which you want to explain the model's prediction. Generate Perturbations: Perturb or slightly modify the features of the selected instance to create new data points. These perturbations could involve adding noise, masking certain features, or making small changes to the original data. Predictions: Use the machine learning model to predict the outcomes for each of the perturbed data points. Fit Interpretable Model: Fit an interpretable model (such as a linear model) to the perturbed data points, where the features and their variations are used as input, and the corresponding model predictions are used as output. Weighting: Weight the importance of each perturbed data point based on how similar it is to the original instance. This weighting is often based on a similarity measure like cosine similarity or Euclidean distance. Interpretation: Analyze the coefficients or weights of the interpretable model to understand the influence of different features on the model's prediction for the selected instance.