College Student Entrepreneurship Risk Assessment Model Based on XGBoost
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Abstract
The core challenge of college student entrepreneurship risk assessment is that traditional models often ignore complex factor interactions, resulting in biased risk predictions. To address this issue, this paper applies an XGBoost model with SHAP-based interpretation. The implementation includes systematic data preprocessing, including cleaning, encoding, and missing-value imputation, followed by a recursive feature elimination strategy combined with XGBoost feature importance to select the optimal subset of predictive variables. Model hyperparameters are optimized through grid search and cross-validation. Finally, the SHAP framework is used to calculate the marginal contribution of each feature and generate global and local attribution graphs, revealing key drivers of risk prediction. Experimental results confirm the effectiveness of the proposed model. In multi-scenario comprehensive evaluation, the average accuracy exceeds 0.85, the misclassification rate is below 0.15, the F1-score exceeds 0.87, Cohen’s Kappa exceeds 0.71, and crossdisciplinary generalization is strong, with AUC exceeding 0.91. The study demonstrates that the XGBoost-SHAP framework improves the reliability, transparency, and interpretability of entrepreneurship risk assessment under complex nonlinear feature interactions.
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