Table 2 Results of hyper-parameter optimization of different machine learning algorithms

XGBoost

{‘max_depth’: [2, 3, 5–7, 9, 12, 15, 17, 25], ‘min_child_weight’: [1, 3, 5, 7], ‘gamma’:[ 0, 0.05 ,0.1,0.2, 0.3, 0.5, 0.7, 0.9, 1], ‘subsample’:[ 0.6, 0.7, 0.8, 0.9, 1], ‘colsample_bytree’:[0.6, 0.7, 0.8, 0.9, 1], ‘learning_rate’:[0.01, 0.015, 0.025, 0.05, 0.1]}

{‘max_depth’: [2], ‘min_child_weight’: [3],

‘gamma’:[0.2],

‘subsample’:[ 0.7], ‘colsample_bytree’:[0.8],

‘learning_rate’:[0.01]}

0.903

0.844

GNB

/

/

0.797

0.808

NN

{‘alpha’: [0.1, 0.01, 0.001, 0.0001], ‘hidden_layer_sizes’:[(50,),(100,)], ‘solver’:[‘sgd’, ‘adam’], ‘activation’:[‘tanh’,‘relu’], ‘learning_rate’:[‘constant’, ‘adaptive’]}

{‘activation’: ‘tanh’, ‘alpha’: 0.1, ‘hidden_layer_sizes’:(50,),

‘learning_rate’: ‘constant’, ‘solver’: ‘adam’}

0.855

0.822

Ridge

{‘alpha’: [0.001, 0.01, 0.1, 1, 10, 100, 1000],‘solver’:[‘svd’, ‘cholesky’, ‘lsqr’, ‘sparse_cg’, ‘sag’, ‘saga’]}

{‘alpha’: 10,

‘solver’: ‘svd’}

0.829

0.836

LR

{‘C’: [0.001, 0.01, 0.1, 1, 10, 100], ‘penalty’:[‘l2’], ‘solver’: [‘newton-cg’, ‘lbfgs’, ‘liblinear’, ‘sag’, ‘saga’]}

{‘C’: 0.1, ‘penalty’: ‘l2’, ‘solver’: ‘newton-cg’}

0.833

0.850