Скачать или смотреть Understanding Bayesian Optimization

Bayesian Optimization is a powerful tool used to optimize complex functions that are expensive to evaluate. At its core, it involves building a probabilistic model of the function you want to optimize and using this model to make decisions about where to evaluate the function next. This method is particularly effective in machine learning for tuning hyperparameters, where traditional methods like grid search or random search fall short due to their inefficiency and high computational cost. Bayesian Optimization leverages the Bayesian statistical approach, which combines prior knowledge with observed data to make predictions. This technique is especially useful in scenarios where each function evaluation is costly, such as training a neural network. By intelligently selecting the most promising hyperparameters to evaluate, Bayesian Optimization can significantly reduce the time and resources required to find optimal settings, making it a valuable tool in any data scientist's toolbox.