SMOTE generates new synthetic samples by interpolating between existing minority class instances. For each instance in the minority class, the algorithm selects one or more of its nearest neighbors and creates new samples along the line segments joining the instance with its neighbors. This helps in expanding the decision boundary of the minority class, making it more comparable to the majority class.
