Table 5.

Hyperparameter optimization strategy and optimal configurations.

Model	Hyperparameters Tuned	Search Space	Optimization Method	Optimal Hyperparameters
Gradient Boosting (GBR)	n_estimators, learning_rate, max_depth	n_estimators ∈ {100, 200, 300}; learning_rate ∈ {0.01, 0.05, 0.1}; max_depth ∈ {3, 5, 7}	Grid Search (5-fold CV)	n_estimators = 200, learning_rate = 0.1, max_depth = 5
XGBoost	n_estimators, learning_rate, max_depth, subsample	n_estimators ∈ {100, 200, 500}; learning_rate ∈ {0.01, 0.1, 0.2}; max_depth ∈ {3, 5, 7}; subsample ∈ {0.7, 0.8, 1.0}	Randomized Search (5-fold CV, 10 iterations)	n_estimators = 200, learning_rate = 0.1, max_depth = 5, subsample = 0.8
LightGBM	n_estimators, learning_rate, max_depth, num_leaves	n_estimators ∈ {100, 200, 500}; learning_rate ∈ {0.01, 0.05, 0.1}; max_depth ∈ {3, 5, 7}; num_leaves ∈ {31, 50, 100}	Grid Search (5-fold CV)	n_estimators = 500, learning_rate = 0.05, max_depth = 5, num_leaves = 50
CatBoost	iterations, learning_rate, depth, l2_leaf_reg	iterations ∈ {200, 500}; learning_rate ∈ {0.01, 0.05, 0.1}; depth ∈ {6, 8, 10}; l2_leaf_reg ∈ {1, 3, 5}	Grid Search (5-fold CV)	iterations = 500, learning_rate = 0.05, depth = 8, l2_leaf_reg = 3
Neural Network (MLP)	Number of hidden units	units ∈ {32, 64, 96, 128}	Keras Tuner (Random Search)	Hidden units = 64, 32; epochs = 50; Adam optimizer
LSTM	Number of LSTM units	Fixed (empirical selection)	Manual tuning	LSTM units = 50; Dense = 25; epochs = 50
Proposed Stacked Regressor	Meta-learner α (Ridge)	α ∈ {0.1, 1.0, 10.0}	Grid Search (5-fold CV)	Ridge α = 1.0