Keywords
Hyperparameter Tuning
Machine Learning
Exploitation
Gaussian Processes
Acquisition Functions
Sequential Model-Based Optimization
Surrogate Model
How to Cite
Abstract
Bayesian optimization is a powerful approach for hyperparameter tuning in machine learning models. It offers a principled framework for balancing exploration and exploitation, leading to efficient search in the hyperparameter space. This paper provides an overview of Bayesian optimization techniques and their application to hyperparameter tuning. We discuss the underlying principles of Bayesian optimization, compare it with other hyperparameter tuning methods, and present case studies highlighting its effectiveness. Additionally, we explore recent advancements and future directions in Bayesian optimization for hyperparameter tuning.
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