IBM Data Science Practice Test 2025 – Comprehensive Exam Prep

Addressing issues with minority classes in a dataset is essential for building effective machine learning models, especially when dealing with imbalanced datasets. Implementing oversampling or undersampling techniques directly tackles uneven class distributions.

Oversampling involves increasing the representation of the minority class by duplicating existing instances or creating synthetic samples, which helps to balance the dataset and allows the model to learn better from the minority class. On the other hand, undersampling reduces the majority class to make it more proportional to the minority class, which can also improve the model's ability to recognize patterns within the minority class.

By using these techniques, you ensure that the model does not become biased towards the majority class and can effectively learn to identify instances from the minority class, leading to improved performance metrics like recall and F1 score for that class. This strategy is a proactive step towards achieving a more balanced and fair model outcome.