Development and validation of a postoperative prediction model for residual stones after hepatectomy in patients with hepatolithiasis

Figure 2 Candidate-model selection within the development cohort. A: Out-of-fold area under the receiver operating characteristic curve (AUC) distributions for 12 candidate algorithms within the development cohort; B: Prediction error (Brier score and log loss) and calibration metrics across candidate pipelines; C: Outer-fold AUC rank variability during nested cross-validation. The held-out validation cohort remained strictly isolated during model evaluation and pipeline locking. LASSO-LR: Least absolute shrinkage and selection operator-logistic regression; XGBoost: EXtreme Gradient Boosting; GBM: Gradient boosting machine; RF: Random forest; CatBoost: Categorical boosting; LDA: Linear discriminant analysis; NB: Naive Bayes; RBF-SVM: Support vector machine with radial basis function kernel; PLSR: Partial least squares regression; LightGBM: Light gradient boosting machine; ANN: Artificial neural network; k-NN: K-nearest neighbors; AUC: Area under the receiver operating characteristic curve.