Bayesian Optimization for Model Tuning in Scientific Research

In addition, Bayesian optimization (BO) is used for model tuning by defining different values for different hyperparameters to construct the parameter space. The parameter combination with the minimum loss function is determined as the optimal parameter combination for the model (Brochu et al., 2010). The classical grid search algorithm exhaustively searches all parameter combinations in the parameter space to determine the optimal parameters, but it is computationally expensive and inefficient. In contrast, the Bayesian optimization algorithm relies on a limited number of observed points and uses a probabilistic surrogate model to estimate the distribution of the loss function. It uses an appropriate acquisition function to select the next observed point, continuing until the iteration is completed or computational resources are exhausted, making it more advantageous in high-dimensional spaces.