Unveiling hidden biases in machine learning feature importance  

作　　者：Yoshiyasu Takefuji 

机构地区：[1]Faculty of Data Science,Musashino University,3-3-3 Ariake Koto-ku,Tokyo 135-8181,Japan

出　　处：《Journal of Energy Chemistry》2025年第3期49-51,共3页能源化学(英文版)

摘　　要：Nirmal et al.presented a machine learning-based design of ternary organic solar cells,utilizing feature importance[1].This paper highlights the alarming potential biases in the use of feature importance in machine learning,which can lead to incorrect conclusions and outcomes.Many scientists and researchers including Nirmal et al.are unaware that feature importances in machine learning in general are model-specific and do not necessarily represent true associations between the target and features.

关 键 词：Machine learning Feature importance Potential bias Chi-squared and P-value 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]