What is a potential drawback of not addressing imbalanced datasets?

When dealing with imbalanced datasets, one significant drawback is that models may fail to recognize the minority class effectively. In classification tasks, if one class is heavily overrepresented compared to another, machine learning models can become biased toward the majority class. As a result, the model may primarily learn to identify examples from the majority class, neglecting the minority class entirely. This leads to high accuracy on the majority class while performing poorly in predicting instances of the minority class.

This issue can be particularly problematic in applications where the minority class is of high interest, such as in fraud detection or medical diagnosis, where failing to identify the minority instances can have serious consequences. Thus, without proper methods to address this imbalance—such as resampling techniques or cost-sensitive learning—models risk underperformance in detecting and predicting the outcomes associated with the minority class, leading to a skewed understanding of the problem at hand.