2026-10-07T01:40:06Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33988101 2026-09-24T17:33:15Z category_734 category_931 category_106 portal_5 item_type_3 month_year_09_2026

<p>Training and inference times of the evaluated machine learning models.</p> Qiang Yin (600539) Biological Sciences not elsewhere classified Information Systems not elsewhere classified Science Policy traditional models attained shapley additive explanations publicly available dataset providing technical support predicting student achievement outperforming traditional models data fitted network predicting academic performance achieving performance comparable improving educational outcomes existing predictive models additional case study exhibiting stronger robustness div >< p 2 </ sup shap analysis reveals suboptimal performance study introduces existing algorithms educational domain “ black sized datasets significant importance secondary education sample sizes results demonstrate personalized instruction model ’ learning behavior future research fold cross employed 10 dual challenges decisive role attendance rate 500 samples 4000 ), <p>Training and inference times are reported in seconds. SVR, support vector regression; KNN, k-nearest neighbors; Ridge, ridge regression; GBM, gradient boosting machine; Random Forest, random forest; XGBoost, extreme gradient boosting; LightGBM, light gradient boosting machine; TabPFN, tabular prior-data fitted network.</p> <p>(XLSX)</p> 2026-09-24T17:33:12Z Dataset Dataset 10.1371/journal.pone.0328634.s002 https://figshare.com/articles/dataset/_p_Training_and_inference_times_of_the_evaluated_machine_learning_models_p_/33988101 CC BY 4.0