Two default implementations of HyperparameterOptimization exist: GridSearch and OptunaOptimization GridSearch is an exhaustive search algorithm, that evaluates all combinations of the specified hyperparameters. OptunaOptimization is a form of Bayesian optimization using Optuna's Tree-structured Parzen Estimator algorithm as the acquisition function. Bayesian optimization is used for iteratively proposing new hyperparameters for a machine learning model applying the Bayesian principle, which allows for finding the optimal hyperparameter combinations without an exhaustive search. The user needs to set the number of iterations because it is an iterative process. By default the search starts with a random sample of ten parameter combinations, afterwards, the acquisition function is used.