Table_1_Predicting 24-hour intraocular pressure peaks and averages with machine learning.DOCX حسب Ranran Chen (3308463)

Table_2_Predicting 24-hour intraocular pressure peaks and averages with machine learning.DOCX حسب Ranran Chen (3308463)

Table_2_Predicting 24-hour intraocular pressure peaks and averages with machine learning.DOCX حسب Ranran Chen (3308463)

Table_1_Predicting 24-hour intraocular pressure peaks and averages with machine learning.DOCX حسب Ranran Chen (3308463)

2000–2020 Monthly Air Quality Index (AQI) Dataset of China حسب Chaohao Ling (19840471)

"…Four tree-based ensemble algorithms (Random Forest [RF], Gradient Boosting Machine [GBM], CatBoost, XGBoost) were compared, with the RF model selected as optimal (test set: R² = 0.83, Root Mean Square Error [RMSE] = 10.25, Mean Absolute Error [MAE] = 9.03) after validation via 10-fold geographic stratified cross-validation and 100 bootstrap iterations; Recursive Feature Elimination (RFE) further refined 14 core predictors to minimize overfitting. …"