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Dengxiang Ji

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Articles

Prediction and interpretation of comprehensive performance of large-scale instruments in universities using interpretable machine learning

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By Yongchun Chen, Dengxiang Ji
| Nature Verified
Abstract To promote data-driven management of large-scale scientific instruments in universities, this study builds a predictive model for the instruments’ comprehensive performance score (CPS) and identifies its core influencing factors by integrating machine learning (ML) and interpretable analysis. Based on operational data of 223 large-scale instruments across 18 departments of a university, seven core indicators were selected as input features.
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