  This paper investigates the impact of feature encoding techniques on the
explainability of XAI (Explainable Artificial Intelligence) algorithms. Using a
malware classification dataset, we trained an XGBoost model and compared the
performance of two feature encoding methods: Label Encoding (LE) and One Hot
Encoding (OHE). Our findings reveal a marginal performance loss when using OHE
instead of LE. However, the more detailed explanations provided by OHE
compensated for this loss. We observed that OHE enables deeper exploration of
details in both global and local contexts, facilitating more comprehensive
answers. Additionally, we observed that using OHE resulted in smaller
explanation files and reduced analysis time for human analysts. These findings
emphasize the significance of considering feature encoding techniques in XAI
research and suggest potential for further exploration by incorporating
additional encoding methods and innovative visualization approaches.
