This article explores the application of Laplace approximated Bayesian optimization for hyperparameter tuning, focusing on regularization techniques in machine learning models. The author discusses the challenges of hyperparameter optimization, particularly in high-dimensional spaces, and presents a case study using logistic regression with L2 regularization. The article compares grid search and Bayesian optimization methods, highlighting the advantages of the latter in efficiently finding optimal regularization coefficients. It also explores the potential for individualized regularization parameters for different variables.
