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Explainable AI (XAI) Technique: LIME (Local Interpretable Model-agnostic Explanations)

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4 min readNov 23, 2023

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Photo by Warren Umoh on Unsplash

LIME (Local Interpretable Model-agnostic Explanations) is another popular technique for explaining the predictions of machine learning models, and it focuses on providing local, interpretable explanations for individual predictions. LIME approximates the complex model’s behavior by training a simpler, interpretable model on perturbed instances of the input data.

I. Key Concepts:

1. Local Interpretability:

  • LIME aims to provide explanations for a specific instance rather than the entire model. It generates locally faithful approximations that help understand the model’s decision-making process for a given prediction.

2. Perturbation and Sampling:

  • LIME perturbs the input features of a specific instance to create a dataset of perturbed instances. It then samples predictions from the complex model on these perturbed instances.

3. Local Interpretable Model:

  • A local interpretable model, often a simple linear model, is trained on the perturbed instances and their corresponding complex model predictions. This interpretable model serves as a proxy for the complex model locally.

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