under-sampling majority class

**Under-Sampling Majority Class** is the **class imbalance technique that reduces the majority class by removing samples** — creating a balanced training set by discarding excess majority examples, trading off majority class information for balanced training. **Under-Sampling Methods** - **Random Under-Sampling**: Randomly remove majority samples — simple but loses information. - **NearMiss**: Select majority samples close to minority decision boundaries — keep the informative ones. - **Tomek Links**: Remove majority samples that form Tomek links (closest pairs of opposite classes) — clean decision boundary. - **Cluster Centroids**: Cluster majority samples and keep only centroids — preserves distribution structure. **Why It Matters** - **Fast Training**: Smaller balanced dataset trains much faster than the full imbalanced dataset. - **Information Loss**: The main drawback — discarding majority samples loses potentially useful information. - **Complementary**: Often combined with over-sampling (SMOTE + Tomek Links) for better results. **Under-Sampling** is **trimming the majority** — reducing dominant class samples to create a balanced training set at the cost of some information loss.