# Explainability of Machine Learning Models under Missing Data

**Authors:** Tuan L. Vo, Thu Nguyen, Hugo L. Hammer, Michael A. Riegler, Pal Halvorsen  
**arXiv:** [2407.00411](https://arxiv.org/abs/2407.00411)  
**Date:** 2024-06-29  
**Summary page:** https://www.summarizepaper.com/en/paper/2407.00411v1/

## Key points

- Study by Tuan L. Vo et al. focuses on impact of imputation methods on calculating Shapley values in Explainable Artificial Intelligence
- Comparison of different strategies and evaluation of effects on feature importance and interaction determined by Shapley values
- Potential biases introduced by imputation methods affecting interpretability of machine learning models
- Lower test prediction mean square error (MSE) does not necessarily imply lower MSE in Shapley values and vice versa
- Xgboost can handle missing data directly, but using it on incomplete data can significantly impact interpretability compared to imputing before training
- Emphasis on considering imputation effects when interpreting models for robust insights and offering practical guidance for selecting appropriate techniques based on dataset characteristics and analysis objectives
- Different approaches leading to varying interpretations, highlighting the need to consider both accuracy and interpretability preservation when dealing with missing data in machine learning models within Explainable Artificial Intelligence frameworks

## Summary

The study by Tuan L. Vo et al. delves into the impact of various imputation methods on calculating Shapley values in Explainable Artificial Intelligence. The researchers compare different strategies and evaluate their effects on feature importance and interaction as determined by Shapley values. The findings highlight the potential biases introduced by imputation methods, affecting the overall interpretability of machine learning models. Interestingly, a lower test prediction mean square error (MSE) does not necessarily imply a lower MSE in Shapley values and vice versa. Additionally, while Xgboost can handle missing data directly, using it on incomplete data can significantly impact interpretability compared to imputing before training. This research emphasizes considering imputation effects when interpreting models for robust insights and offers practical guidance for selecting appropriate techniques based on dataset characteristics and analysis objectives. It also sheds light on how different approaches can lead to varying interpretations and highlights the need to carefully consider both accuracy and interpretability preservation when dealing with missing data in machine learning models within Explainable Artificial Intelligence frameworks.