Description: Under-sampling is a technique used in data preprocessing that aims to balance an imbalanced dataset by reducing the number of instances in the majority class. This process is crucial in the fields of machine learning and data mining, where models can be biased towards the class with greater representation, negatively impacting their performance and generalization ability. By applying under-sampling, the goal is to create a more balanced dataset that allows learning algorithms to learn more effectively and fairly. This technique may involve randomly removing instances from the majority class or selecting a representative subset of these instances. Under-sampling not only improves model accuracy but can also reduce training time and computational complexity, resulting in a more efficient process. However, it is important to apply this technique carefully, as excessive reduction can lead to the loss of valuable information and, consequently, poor model performance. In summary, under-sampling is an essential tool in data preprocessing that helps address the issue of imbalance in datasets, thereby enhancing the effectiveness of machine learning models.
