In this article, I will be talking about the population-based training hyperparameter method with an example. You can refer to my previous article to learn more about hyperparameter tuning.
Hyperparameter tuning is a critical aspect of machine learning model development that involves finding the optimal combination of hyperparameters to achieve the best performance for a given dataset. Traditional grid search and random search methods are often time-consuming and inefficient, especially when dealing with complex models and large datasets. To address these challenges, population-based training (PBT) has emerged as an effective approach to hyperparameter tuning. In this article, we will delve into the concept of PBT and its advantages and provide a detailed example using the XGBoost algorithm.