Machine learning and radiomics for differentiating severe from moderately severe acute necrotizing pancreatitis on contrast-enhanced computed tomography

Table 9 Final hyperparameter values

Model	Hyperparameter	Value
SVM	Kernel, C, gamma	Poly, 0.2
RF	n_estimators, max_depth, min_samples_split, max_features	50, 5, 15, 10
KNN	n_neighbors, wights, p	30, distance, 1
GBDT	n_estimators, learning_rate, max_depth, subsample	20, 0.3, 3, 0.8
XGBoost	n_estimators, learning_rate, max_depth, subsample	20, 0.1, 5, 0.5
LightGBM	n_estimators, learning_rate, num_leaves, colsample_bytree, subsample, max_depth	4000, 0.08, 32 (25), 0.65, 0.9, 5

SVM: Support vector machine; RF: Random forest; KNN: k-nearest neighbor; GBDT: Gradient boosting decision tree; XGBoost: Extreme gradient boosting; LightGBM: Light gradient boosting machine.