Prediction and Interpretability Analysis of Quality Evaluation for “Double High Plan” Colleges based on Random Forest Model and SHAP Value

DOI

10.2991/978-2-38476-497-6_15How to use a DOI?

Keywords

“Double High Plan” colleges; Quality evaluation; Random forest; SHAP; Feature visualization

Abstract

An interpretability model based on random forest and SHAP values was developed, simultaneously achieving good prediction and interpretation ability, aiming to support the interpretability prediction of quality evaluation for “Double High Plan” colleges. Data were collected from the quality reports of 227 colleges, and comparisons were conducted between the random forest model and four alternative machine learning models. The results showed that the random forest model’s prediction performance was better than the other models, with a weighted-average precision of 0.8560 and an area under precision recall curve of 0.9181. Thus, it was selected as the best prediction model. Using SHAP value-based visual interpretation, 30 important indicators that significantly affect quality evaluation were identified through feature analysis, enhancing model transparency and providing reliable evidence for the quality evaluation of “Double High Plan” colleges.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Weijun Dai
AU  - Bo Xie
AU  - Yuanyuan Liu
PY  - 2025
DA  - 2025/12/15
TI  - Prediction and Interpretability Analysis of Quality Evaluation for “Double High Plan” Colleges based on Random Forest Model and SHAP Value
BT  - Proceedings of the 2025 International Conference on Educational Innovation and Information Technology (EIIT 2025)
PB  - Atlantis Press
SP  - 149
EP  - 156
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-497-6_15
DO  - 10.2991/978-2-38476-497-6_15
ID  - Dai2025
ER  -