TPE-optimized XGBoost model for fine-grained prediction of tertiary vocational college students' employment destinations.

Predicting tertiary vocational college students' 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' employment destinations. The dataset comprises 2,270 graduates (2016-2019 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.