Скачать или смотреть Using Bayesian Optimization for Advanced Tuning

Bayesian Optimization is an advanced method for hyperparameter tuning that is particularly effective for complex search spaces. Unlike traditional methods such as grid or random search, Bayesian Optimization builds a probabilistic model of the objective function and uses it to select the most promising hyperparameters. This approach can lead to more efficient exploration and faster convergence to optimal solutions. Implementing Bayesian strategies involves using a surrogate model to predict performance outcomes and iteratively updating these predictions as new data is obtained. By leveraging Bayesian Optimization, you can handle complex search spaces more effectively and enhance model performance beyond conventional methods.