Chapter 8, Local Interpretable Model-Agnostic Explanations (LIME)

1. LIME stands for Local Interpretable Model-agnostic Explanations. (True|False)

   True.

2. LIME measures the overall accuracy score of a model. (True|False)

   False. LIME's unique approach measures the truthfulness of a prediction locally.

3. LIME's primary goal is to verify if a local prediction is faithful to the model. (True|False)

   True.

4. The LIME explainer shows why a local prediction is trustworthy or not. (True|False)

   True.

5. If you run a random forest model with LIME, you cannot use the same model with an extra trees model. (True|False)

   False. LIME's algorithm is model-agnostic.

6. There is a LIME explainer for each model. (True|False)

   False. LIME's explainer applies to a range of models.

7. Prediction metrics are not necessary if a user is satisfied with predictions. (True|False...