How can you address imbalanced datasets when tuning hyperparameters?
Imbalanced datasets are a common challenge in machine learning, especially for classification problems. They occur when one class has significantly more samples than another, which can lead to biased models that favor the majority class. To address this issue, you need to consider how to balance your data and how to tune your hyperparameters, which are the settings that control the behavior and performance of your learning algorithm. In this article, you will learn some strategies and tips to deal with imbalanced datasets when tuning hyperparameters.