Table 2.

Hyperparameter tuning settings for each classifier.

Classifier	Hyperparameters
ANN	hidden_layer_sizes: [(50, 50), (100, 100)], alpha: [0.0001, 0.001, 0.01]
SVM	C: [0.1, 1, 10], kernel: [‘linear’, ‘rbf’], gamma: [’scale’, ’auto’]
XGB	learning_rate: [0.01, 0.1, 0.2], n_estimators: [100, 200, 300]
RF	n_estimators: [50, 100, 200], max_depth: [None, 10, 20], min_samples_split: [2, 5, 10]
KNN	n_neighbors: [3, 5, 7], weights: [‘uniform’, ‘distance’]
LR	C: [0.1, 1, 10], penalty: [‘l2’, ‘none’]LDA solver: [‘svd’, ‘lsqr’, ‘eigen’]