Case Study: Local Explanations for Regression Problem with LIME

Local interpretable model-agnostic explanations (LIME) is a framework for explaining the predictions of machine learning models, particularly black box models.

It works by training a locally interpretable model around a specific prediction to approximate the behavior of the complex model in the vicinity of that prediction.

It generates human-interpretable explanations that help users understand why a model made a particular prediction for a given instance. This framework is valuable for improving transparency and trust in machine learning applications, especially when the inner workings of complex models are not easily understandable.

LIME can be used with any machine learning model because it is not specific to any particular model. It helps us understand the model by changing the input data samples and seeing how the predictions change.

In this lesson, we create a regression model that predicts house prices using a housing dataset. We then use the LIME framework to explain how the model makes predictions for each individual example.