Local Interpretable Model-agnostic Explanations (LIME)
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.
Sections
2.1 Intuition Behind LIME
2.2 How LIME Works: Perturbation and Surrogate Models
2.3 Applying LIME to Tabular Data
2.4 Applying LIME to Text Data
2.5 Interpreting LIME Explanations
2.6 LIME Implementation with Python
2.7 Limitations and Considerations for LIME
2.8 Hands-on Practical: Generating LIME Explanations
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