Table · dataset · 2026

<p>Performance of four classification models (XGBoost, Gradient Boosting, Logistic Regression, and Random Forest) in predicting depression within each latent cluster. Values represent the mean area under the ROC curve (AUC) along with 95% confidence intervals (CI) computed on held-out test sets. Models were trained using the top 30 cluster-specific features identified by permutation feature importance (PFI), with class imbalance addressed via SMOTE. XGBoost consistently achieved the highest AUC across all clusters.</p>

Listed in figshare and Loughborough Research Repository — shown once because both records carry DOI 10.1371/journal.pdig.0001719.t002

<p>Performance of four classification models (XGBoost, Gradient Boosting, Logistic Regression, and Random Forest) in predicting depression within each latent cluster.