Machine learning models often involve a complex interplay of hyperparameters, which significantly affect their performance. Selecting the right combination of hyperparameters is a crucial step in building robust and accurate models. Traditional methods like grid search and random search are popular but can be inefficient and time-consuming. Distributed Evolutionary Hyperparameter Tuning (DEHB) is an advanced technique that offers several advantages, making it a compelling choice for hyperparameter optimization tasks. In this article, we will delve into DEHB using the popular XGBoost algorithm and provide Python code examples for each step of the process.
Why Hyperparameter Tuning Is Important
Hyperparameter tuning plays a pivotal role in the machine learning model development process for several reasons: