Question

Which of the following are typically true when comparing the LIME tool with the SHAP tool?

1) LIME is less accurate than SHAP.
2) LIME supports fewer types of models than SHAP.
3) LIME outputs results faster than SHAP.
4) LIME supports more programming languages than SHAP.

Answer 1

When comparing LIME and SHAP, LIME is seen as less accurate due to its local approximation method, whereas SHAP provides a more theoretically solid contribution measure using Shapley values. Option 1 is correct.

Step-by-step explanation:

The student is asking about the comparison between two popular model interpretability tools: LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations). Both tools are used to explain the predictions of machine learning models, but they have different characteristics and implementations.

Comparisons between LIME and SHAP:

LIME is generally considered to be less accurate than SHAP because LIME approximates model predictions locally and may not capture the true model behavior as effectively as SHAP, which uses Shapley values to provide a more precise and theoretically grounded measure of feature attribution.

LIME does not inherently support fewer model types than SHAP. In fact, as a model-agnostic tool, it can be applied to any model, though compatibility can vary based on the implementation.

LIME may output results faster than SHAP due to its simplified local linear approximation approach, which is typically less computationally intensive than SHAP's method of calculating Shapley values.

The support for different programming languages is more about the specific libraries available for LIME and SHAP, rather than the methods themselves. Both tools primarily support Python, which is the dominant language in the machine learning community.