{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T17:58:40Z","timestamp":1790618320526,"version":"4.1.0"},"reference-count":44,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010834","name":"Education Department of Hainan Province","doi-asserted-by":"publisher","award":["Hnky2025ZC-30"],"award-info":[{"award-number":["Hnky2025ZC-30"]}],"id":[{"id":"10.13039\/501100010834","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010834","name":"Education Department of Hainan Province","doi-asserted-by":"publisher","award":["Hnjg2026ZD\u201370"],"award-info":[{"award-number":["Hnjg2026ZD\u201370"]}],"id":[{"id":"10.13039\/501100010834","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010834","name":"Education Department of Hainan Province","doi-asserted-by":"publisher","award":["Hnky2026ZD\u201321"],"award-info":[{"award-number":["Hnky2026ZD\u201321"]}],"id":[{"id":"10.13039\/501100010834","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021192","name":"Key Laboratory for the Synthesis and Application of Organic Functional Molecules, Ministry of Education","doi-asserted-by":"publisher","award":["HNJMT2023\u2013101"],"award-info":[{"award-number":["HNJMT2023\u2013101"]}],"id":[{"id":"10.13039\/501100021192","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Predicting tertiary vocational college students\u2019 employment destinations has become an urgent need for most tertiary vocational colleges. This study proposes a machine learning framework based on Tree-structured Parzen Estimator (TPE)-Optimized eXtreme gradient boosting (XGBoost) for fine-grained prediction of tertiary vocational college students\u2019 employment destinations. The dataset comprises 2,270 graduates (2016\u20132019 cohorts) from a single tertiary vocational college in China, characterized by 27 features spanning demographics, entrance scores, academic records, and extracurricular engagement. Employment destinations are categorized into three fine-grained classes: smooth employment, further study, and unemployment (class distribution: 78.8%, 17.8%, 3.4%). Recursive Feature Elimination (RFE) reduced the feature set to 22 informative variables. TPE hyperparameter optimization (100 iterations) was applied to the XGBoost classifier using an 80\/20 train-test split, with stratified 5-fold cross-validation to mitigate overfitting risk. The proposed model achieves an overall accuracy of 81.72% and a balanced accuracy of 48.80% on the held-out test set, with macro-averaged precision of 0.6608, macro-averaged recall of 0.4880, macro-averaged specificity of 0.7659, and macro-averaged F1-score of 0.5263, outperforming seven baseline classifiers, including non-optimized XGBoost, logistic regression (LR), decision tree (DT), support vector machine (SVM), random forest (RF), K-nearest neighbor (KNN), and LightGBM. SHAP analysis identifies academic GPA across multiple semesters, Gaokao scores, and volunteer service hours as the most influential predictors.<\/jats:p>","DOI":"10.1371\/journal.pone.0359421","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T17:33:17Z","timestamp":1790616797000},"page":"e0359421","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["TPE-optimized XGBoost model for fine\u2013grained prediction of tertiary vocational college students\u2019 employment destinations"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-0367-7163","authenticated-orcid":true,"given":"Juntao","family":"Chen","sequence":"first","affiliation":[{"name":"Hainan College of Economics and Business","place":["Haikou, China"],"department":["School of Financial Management"]},{"name":"Macao Polytechnic University","place":["Macao, 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