武藤 佳恭

Li et al. (2025) highlighted Random Forest's (RF) high accuracy and SHapley Additive exPlanations (SHAP)-derived feature importance for almond deterioration. However, concerns persist regarding the reliability of these interpretations, as high predictive accuracy doesn't guarantee valid feature rankings due to inherent biases in tree-based models, further amplified by SHAP's model dependency. To mitigate this, integrating robust statistical methods such as Spearman's rho, Kendall's tau, Total correlation and Effective transfer entropy is crucial for unbiased assessment. This combined approach ensures a more reliable evaluation of key indicators. Future research should prioritize methodologies combining machine learning with rigorous statistical validation for more interpretable and trustworthy insights in complex biological systems. This integrated approach holds significant promise for improving the reliability of feature importance evaluations, leading to more trustworthy insights applicable to food science and chemistry fields.

This correspondence critically examines the methodology of Schindele et al. (2025) on thyroid cancer recurrence prediction. While their interpretable XGBoost model achieved a high predictive accuracy of 95.8% and a 0.947 AUROC, it is crucial to recognize that this predictive power does not justify the reliability of its derived feature importance rankings. As widely acknowledged in the literature, high predictive accuracy does not guarantee unbiased or reliable feature attribution. We underscore that gradient boosting decision tree (GBDT) models, including XGBoost, are prone to inherent biases in feature importance estimation, often due to overfitting. Furthermore, SHapley Additive exPlanations (SHAP), a widely adopted explainable AI (XAI) technique, can inherit and even amplify these biases, given its model-dependent nature. This raises concerns about the interpretive validity of the identified risk factors. To mitigate these methodological limitations, we advocate for integrative analytical frameworks that combine machine learning with robust statistical and non-parametric approaches, such as Highly Variable Feature Selection (HVFS) and Independent Component Analysis (ICA). These multi-faceted strategies are indispensable for obtaining robust and interpretable insights into feature importance, warranting their prioritization in future research efforts.