Bayesian Optimization for Efficient Model Tuning: An Alternative to Grid Search

Furthermore, Bayesian Optimization (BO) was employed for model tuning, where various values of different hyperparameters were defined to construct the parameter space. The optimal parameter combination for the model, which minimized the loss function, was determined by this approach (Brochu et al., 2010). \n\nWhile the classical grid search algorithm exhaustively explores all parameter combinations in the parameter space to determine the optimal parameters, this method is computationally expensive and inefficient. In contrast, Bayesian optimization estimates the distribution of the loss function using a probability surrogate model based on a limited number of observation points. It selects the next observation point using an appropriate acquisition function, continuing until the iteration is completed or computational resources are depleted. As a result, Bayesian optimization proves to be more advantageous in high-dimensional spaces.