Understanding the Role of LIME and SHAP in Interpreting Machine Learning Models

by | Mar 19, 2024 | science

Read the following articles:
• Dario Radečić. 2020. “SHAP: How to Interpret Machine Learning Models With Python, Explainable machine learning with a single function call.” https://towardsdatascience.com/shap-how-to-interpret-machine-learning-models-with-python-2323f5af4be9
• Marco Tulio Ribeiro, Sameer Singh, Sameer Singh. 2016. “Why Should I Trust You? Explaining the Predictions of Any Classifier.” https://arxiv.org/pdf/1602.04938v1.pdf
• Dario Radčić. 2020. “LIME vs. SHAP: Which is Better for Explaining Machine Learning Models? Two of the most popular Explainers compared.” https://towardsdatascience.com/lime-vs-shap-which-is-better-for-explaining-machine-learning-models-d68d8290bb16
• Conor O’Sullivan. 2022. “Squeezing more out of LIME with Python How to create global aggregations of LIME weights.” https://towardsdatascience.com/squeezing-more-out-of-lime-with-python-28f46f74ca8e
• Refer to the SHAP documentation as needed: https://shap.readthedocs.io/en/latest/index.html
• Refer to the LIME documentation as needed: https://lime-ml.readthedocs.io/en/latest/
The key idea behind LIME and SHAP is to provide human-understandable explanations for models, especially black box models such as neural networks and support vector machines. For this assignment, you will be providing only written answers in a Word or PDF document. Based on the aforementioned readings, and any others from the course content, answer the following questions providing proper citations in APA format.
Question Points
1. Write a 1-page explanation of what LIME is, using your own words. Your explanation should include what a local interpretable model is and, more specifically, which types of models are used by LIME.
Question Points
2. Write a 1-page explanation of what LIME is, using your own words. Your explanation should include what a local interpretable model is and, more specifically, which types of models are used by LIME.
3. Using up to 1-page, compare and contrast LIME vs. SHAP. Include the potential pros and cons of each.
4. Writing quality: Avoid spelling mistakes and clearly denote which questions you are answering. Include a reference section in your writeup, and in your text, refer to the references. Follow the APA guidelines for references and citations.

 

 

Sample Answer

Sample Answer

 

Title: Understanding the Role of LIME and SHAP in Interpreting Machine Learning Models

Introduction

Machine learning models have become increasingly complex, making it challenging for users to understand how these models arrive at their predictions. In response to this issue, methods like Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) have been developed to provide interpretable explanations for black box models. This essay aims to explore and compare LIME and SHAP, highlighting their features, applications, and potential pros and cons.

LIME: Local Interpretable Model-Agnostic Explanations

LIME is a technique designed to explain the predictions of any machine learning model by approximating it with an interpretable model locally. In simpler terms, LIME creates a model that is easier to understand for a specific instance or set of instances rather than explaining the entire complex model. This local interpretable model helps users comprehend why a particular prediction was made by the black box model.

LIME mainly uses simple, interpretable models such as linear regression or decision trees to approximate the black box model locally. By generating perturbations around the instance to be explained and observing the output changes, LIME can determine the relative importance of each feature in making the prediction. Through this process, LIME provides insights into the inner workings of the black box model on a local level.

SHAP: SHapley Additive exPlanations

On the other hand, SHAP is based on cooperative game theory and calculates the contribution of each feature to the prediction by considering all possible permutations of features. This method provides a more global perspective on feature importance compared to LIME’s local explanations. SHAP values offer a unified measure of feature importance that is consistent across different instances.

Comparison between LIME and SHAP

When comparing LIME and SHAP, it is essential to consider their respective strengths and weaknesses:

LIME

– Pros:- Provides local interpretability for individual predictions.
– Utilizes simple and interpretable models for approximation.
– Suitable for explaining complex models in a straightforward manner.

– Cons:- May not capture global feature importance effectively.
– Relies on sampling techniques that can introduce variability in explanations.

SHAP

– Pros:- Offers a consistent measure of feature importance across instances.
– Provides a global view of feature contributions to predictions.
– Based on solid theoretical foundations from cooperative game theory.

– Cons:- Computationally intensive, especially for large datasets and complex models.
– May be harder to interpret for non-experts due to its intricate methodology.

In conclusion, both LIME and SHAP play crucial roles in interpreting machine learning models by providing explanations that are understandable to humans. While LIME focuses on local interpretability using simple models, SHAP offers a more global perspective based on cooperative game theory. Depending on the specific needs of the user, either LIME or SHAP can be chosen to gain insights into the black box models’ decision-making processes.

References

– Radečić, D. (2020). “SHAP: How to Interpret Machine Learning Models With Python, Explainable machine learning with a single function call.” https://towardsdatascience.com/shap-how-to-interpret-machine-learning-models-with-python-2323f5af4be9
– Ribeiro, M. T., Singh, S., & Singh, S. (2016). “Why Should I Trust You? Explaining the Predictions of Any Classifier.” https://arxiv.org/pdf/1602.04938v1.pdf
– Radčić, D. (2020). “LIME vs. SHAP: Which is Better for Explaining Machine Learning Models? Two of the most popular Explainers compared.” https://towardsdatascience.com/lime-vs-shap-which-is-better-for-explaining-machine-learning-models-d68d8290bb16
– O’Sullivan, C. (2022). “Squeezing more out of LIME with Python How to create global aggregations of LIME weights.” Link
– SHAP Documentation: https://towardsdatascience.com/squeezing-more-out-of-lime-with-python-28f46f74ca8e
– LIME Documentation: https://lime-ml.readthedocs.io/en/latest/

 

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