Results of the summarizing process for the arXiv paper: 2204.11351v3

The interpretation of machine learning (ML) models is becoming increasingly important, and SHapley Additive exPlanations (SHAP) is one external method that provides both instance and model-level explanations for deep learning (DL) models. However, the effect of background dataset size on SHAP's stability remains unexplored. In an empirical study using the MIMIC-III dataset, researchers found that SHAP values and variable rankings fluctuate when using different background datasets acquired from random sampling, indicating that users cannot blindly trust SHAP's interpretation. The fluctuations decrease with an increase in the background dataset size. Additionally, there is a U-shape in the stability assessment of SHAP variable rankings, demonstrating that it is more reliable in ranking the most and least important variables compared to moderately important ones. Overall, this study suggests that users should consider how background data affects SHAP results and opt for larger dataset sizes to mitigate fluctuations in SHAP's stability. To ensure reliable results from their interpretations of ML models using SHAP, users should use larger datasets when possible. The code used in this study is publicly accessible.

Understanding the Impact of Background Dataset Size on SHAP's Stability

Interpreting machine learning (ML) models has become increasingly important in recent years, and one external method that provides both instance and model-level explanations is SHapley Additive exPlanations (SHAP). However, the effect of background dataset size on SHAP's stability remains unexplored. In a study published in 2020, researchers used the MIMIC-III dataset to investigate how background data affects SHAP results. The findings suggest that users should consider how background data affects SHAP results and opt for larger datasets when possible to ensure reliable interpretations from their ML models using SHAP.

Background Data Affects SHAP Results

The research team conducted an empirical study using the MIMIC-III dataset to explore how different sizes of background datasets affect the stability of SHAP values and variable rankings. They found that when using different background datasets acquired from random sampling, there were fluctuations in both the values and rankings produced by SHAP. This indicates that users cannot blindly trust what they see from these interpretations without considering other factors such as dataset size.

Larger Datasets Provide More Reliable Results

The researchers also discovered that with an increase in the size of the background dataset, fluctuations decreased significantly. Additionally, they observed a U-shape in terms of stability assessment for variable rankings; this means it was more reliable at ranking variables according to importance compared to moderately important ones. Overall, this study suggests that larger datasets are better suited for providing more reliable results when interpreting ML models with SHAP than smaller ones.

Conclusion & Accessible Code

To conclude, this research demonstrates why it is important for users to consider how background data affects their interpretation of ML models using SHAP before relying on its output completely. Larger datasets provide more stable results than smaller ones; thus, opting for larger datasets can help mitigate any fluctuations or discrepancies seen in outputs from these interpretations. The code used in this study is publicly accessible so anyone interested can replicate or build upon it further if needed.