SMOTE (Synthetic Minority Over-sampling Technique) is a data augmentation method for imbalanced datasets that generates synthetic minority samples by interpolating between existing minority examples — creating new, diverse training examples along the line segments connecting minority class nearest neighbors.

How SMOTE Works

• Select: Choose a minority class sample $x_i$.
• Neighbors: Find its $k$ nearest minority class neighbors.
• Interpolate: $x_{new} = x_i + lambda (x_{nn} - x_i)$ where $lambda sim U(0,1)$ and $x_{nn}$ is a random neighbor.
• Repeat: Generate enough synthetic samples to reach the desired class balance.

Why It Matters

• Diversity: Unlike random duplication, SMOTE creates NEW examples — reduces overfitting risk.
• Feature Space: Interpolation in feature space produces plausible new examples.
• Foundational: SMOTE (Chawla et al., 2002) is the most cited imbalanced learning method — the standard baseline.

SMOTE is creating synthetic minorities — generating new minority examples by interpolating between existing ones for balanced, diverse training.