over-sampling minority class

**Over-Sampling Minority Class** is the **simplest technique for handling class imbalance** — duplicating or generating additional samples from the minority class to increase its representation in the training set, ensuring the model receives sufficient gradient signal from rare classes. **Over-Sampling Methods** - **Random Duplication**: Randomly duplicate existing minority samples — simplest approach. - **SMOTE**: Generate synthetic samples by interpolating between nearest minority neighbors. - **ADASYN**: Adaptively generate more synthetic samples in regions where the minority class is underrepresented. - **GAN-Based**: Use GANs to generate realistic synthetic minority samples. **Why It Matters** - **No Information Loss**: Unlike under-sampling, over-sampling preserves all training data. - **Overfitting Risk**: Exact duplication can cause the model to memorize minority examples — augmentation mitigates this. - **Semiconductor**: Rare defect types need over-sampling — a model that ignores rare defects is operationally dangerous. **Over-Sampling** is **amplifying the rare signal** — increasing minority class representation to ensure the model learns from every class.