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Table 3. Summary of the main hyperparameters and their adopted values for 12 ML models
Model Hyperparameter Parameter value
n_estimators 100
learning_rate 1.0
AdaBoost (decision tree regressor) max_depth 3
min_samples_split 2
min_samples_leaf 1
hidden_layer_sizes (100, 50)
ANN alpha 0.001
max_iter 1000
n_estimators 100
min_samples_split 2
Bagging
min_samples_leaf 1
max_samples 1
min_samples_split 2
min_samples_leaf 1
DT
min_weight_fraction_leaf 0
min_impurity_decrease 0
min_samples_leaf 1
min_weight_fraction_leaf 0
min_impurity_decrease 0
Extra trees ccp_alpha 0
n_estimators 100
max_depth 10
min_samples_split 2
n_estimators 100
max_depth 3
GBRT subsample 1
learning_rate 0.1
min_samples_split 2
n_neighbors 5
KNN leaf_size 30
p 2
num_leaves 31
max_depth -1
LightGBM learning_rate 0.1
n_estimators 100
subsample 1
max_depth -1
learning_rate 0.1
n_estimators 100
RF
subsample 1
min_samples_split 2
min_samples_leaf 1
