Bayesian Optimization, Machine Learning, AI, Hyperparameter Tuning.
objective function. This process is iterative, with each trial recorded to refine our search and find optimal hyperparameters setting. Through this approach, we aim to pinpoint the configuration that minimizes our objective function, thus achieving the highest possible accuracy for the random