Performance metrics for seven classification models in predicting depression were evaluated on the test dataset. Metrics reported include accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC). Random Forest and CatBoost achieved the highest overall accuracy (82.2%), with Random Forest obtaining the highest recall (0.94) and AUC (0.91), indicating its strong ability to identify depressed individuals.
收藏NIAID Data Ecosystem2026-05-10 收录
数据链接：
官方服务：
资源简介：
Performance metrics for seven classification models in predicting depression were evaluated on the test dataset. Metrics reported include accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC). Random Forest and CatBoost achieved the highest overall accuracy (82.2%), with Random Forest obtaining the highest recall (0.94) and AUC (0.91), indicating its strong ability to identify depressed individuals.
创建时间：
2025-11-20
