Machine learning models often require tuning of hyperparameters to achieve their best performance. Hyperparameter tuning can be a daunting and time-consuming task, as it involves experimenting with different parameter combinations to find the optimal settings. Bayesian Optimization and Hyperband (BOHB) is a cutting-edge technique that leverages Bayesian optimization and the Hyperband algorithm to efficiently search for the best hyperparameters for machine learning models. In this article, we will delve into what BOHB is and its advantages and provide a practical example of tuning hyperparameters for an XGBoost model using BOHB.
What Is BOHB?
BOHB stands for Bayesian Optimization and Hyperband. It combines two powerful concepts: