Undersampling

Undersampling is a technique used in data analysis and machine learning to address class imbalance in datasets. In many classification problems, one class may be significantly underrepresented compared to another (or others). This imbalance can lead to biased models that perform poorly on the minority class. Here's a brief overview of the undersampling process and its contexts: 1. **Purpose**: The primary goal of undersampling is to balance the distribution of classes by reducing the number of instances in the majority class.