Description: Imbalanced data refers to a situation where classes in a dataset are not represented equally. This means that some classes have a significantly higher number of examples compared to others. This phenomenon is common in various machine learning applications, especially in classification problems. For instance, in a fraud detection dataset, there may be thousands of legitimate transactions and only a few fraudulent ones. This imbalance can lead machine learning models to lean towards the majority class, resulting in poor performance when classifying the minority class. Neural networks, which are one of the most used techniques in deep learning, can be affected by this issue as they tend to optimize for minimizing global error, which can lead to bias towards the more represented class. In the context of distributed learning, where models are trained on multiple devices with local data, data imbalance can complicate model aggregation and generalization. In data science, it is crucial to identify and address imbalanced data to ensure that models are fair and accurate, especially in critical applications like medicine or security. Therefore, proper handling of imbalanced data is essential for developing robust and reliable models.
