TABLE 2.

Selected Hyperparameters and Classification Thresholds

Tuning
Model	Hyperparametersa	Thresholda,b
LDA	n/ac	0.34
Elastic net	Penalty = 0.0368, mixture = 0.4793	0.32
RF	mtry = 3, min_n = 38	0.45
KNN	Neighbors = 50, weight function = rectangulard	0.31
SVM with linear kernele	Cost = 0.0170, margin = 0.0825	0.34
XGBoost	mtry = 11, min_n = 5, tree_depth = 8, learn_rate = 0.0074, loss_function = 4.6130, sample_size = 0.6757	0.31

^a Hyperparameters are reported to 4 decimal places, performance metrics to 3, and thresholds to 2 for clarity and consistency.

^b Thresholds were selected to maximize the F1 score on cross validation.

^c No hyperparameters are applicable for logistic regression and LDA.

^d Three weighting functions were evaluated for KNN: rectangular (equal weight to all neighbors), Gaussian (exponentially higher weight to closer neighbors), and optimal (automatically selects a kernel function to optimize classification performance).

^e Three kernel types were evaluated for SVM: linear, polynomial, and radial basis function.

cost = penalty for misclassification; KNN = k-nearest neighbors; LDA = linear discriminant analysis; learn_rate = learning rate for boosting; loss_function = objective/loss function used for optimization; margin = margin width for separation; min_n = minimum observations required to split a node; mixture = proportion of L1 vs L2 penalty; mtry = number of predictors sampled for each split; n/a = not applicable; neighbors = number of nearest neighbors; penalty = overall regularization strength; RF = random forest; sample_size = proportion of training data used in each boosting round; SVM = support vector machines; tree_depth = maximum depth of each tree; weight function = distance weighting method; XGBoost = extreme gradient boosting.