Study of the concept of Bayesian optimization and practical use of its algorithms in the Python programming language

The aim of the study is to explore the principles of Bayesian optimization and its potential for solving complex problems, including economic ones. This article presents the main aspects of Bayesian optimization such as selection of a priori distribution, estimation of posterior distribution and selection of optimal model parameters. An example of applying Bayesian optimization to find hyperparameters using the Python programming language is presented. Bayesian optimization algorithms and their application to improve machine learning models were studied. The use of Bayesian optimization algorithm for finding hyperparameters can be useful in the future for optimizing various machine learning models such as neural networks, SVM and others.