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.
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https://figshare.com/articles/dataset/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_achi/30669463
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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
