IBM Data Science Test 2025 – 400 Free Practice Questions to Pass the Exam

The challenge with a minority class in classification is that it may lead to biased predictions. When the minority class has significantly fewer examples compared to the majority class, machine learning models can struggle to learn the characteristics of the minority class adequately. As a result, the model may become biased towards the majority class, which often leads to high accuracy but poor predictive performance, especially for the minority class. This imbalance can result in a system that fails to identify or correctly classify instances from the minority class, potentially overlooking important outcomes or insights.

In practice, this can manifest as a higher number of false negatives when predicting the minority class, which can be particularly detrimental in contexts like fraud detection, medical diagnosis, or any application where the minority class represents cases of critical importance. Addressing this challenge typically involves techniques like resampling, using different evaluation metrics that account for class imbalance, or applying specialized algorithms designed to work better with imbalanced datasets.