What is a common challenge with imbalanced datasets in machine learning?

Imbalanced datasets occur when the classes within the dataset are not represented equally, leading to a scenario where one class (the majority class) vastly outnumbers the others (the minority class). This imbalance can result in algorithms becoming biased toward the majority class because they tend to favor the more frequently represented class when making predictions. Consequently, models trained on such datasets may have high accuracy overall due to the majority class's prevalence while performing poorly in predicting the minority class. This phenomenon makes it difficult to detect the minority class, which is often of greater interest in many applications, such as fraud detection or disease diagnosis.

Focusing on the other choices helps clarify the context: while imbalanced datasets can complicate management and evaluation metrics to some extent, these do not capture the core issue of how machine learning algorithms interpret and learn from the data. Efficient data storage is generally not a problem attributed to class imbalance, so that option isn't relevant to the central challenge posed by the question.