Prevention of Leakage in Machine Learning Prediction for Polymer Composite Properties
Hajime Shimakawa,
Akiko Kumada,
Masahiro Sato
Abstract:Machine learning (ML) has facilitated property prediction for intricate materials by integrating materials and experimental features such as processing and measurement conditions. However, ML models designed for material properties have often disregarded a common issue of "leakage," resulting in an overestimation of model performance and a decrease in model transferability. This issue can arise from biases inherent in multiple data points obtained from the same experimental group. We provide a critical examina… Show more
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