Understanding the overall behavior of a machine learning model is helpful, but often we need to know why a specific prediction was made. How can we explain the output of a complex, opaque model for a single instance? This chapter introduces Local Interpretable Model-agnostic Explanations (LIME), a technique designed specifically for this purpose.

LIME works by approximating the complex model locally around the prediction you want to explain using a simpler, interpretable model. Because it treats the original model as a black box, it can be applied to virtually any classifier or regressor.

In this chapter, you will learn:

• The core intuition behind LIME and how it generates explanations.
• The mechanics of LIME, including data perturbation and the use of local surrogate models.
• How to apply LIME to explain predictions for models trained on tabular and text data.
• Methods for interpreting the feature importance scores and visualizations produced by LIME.
• Practical implementation using the LIME Python library.
• Important considerations and limitations when using LIME.

We will conclude with a hands-on exercise where you apply LIME to generate and analyze explanations for a pre-trained model.