When the target class appears very rarely in the data set, it can reduce the usefulness of the classification models by creating a bias toward the majority class due to the class imbalance. One way to address the imbalance in the dataset is to use $$ to create more instances of the minority class to balance the dataset.

overfitting

oversampling

optional testing

data partitioning