This is the exactly opposite process of under-sampling; here, elements of the minority class are randomly added until the ratio between the majority and minority classes is close enough. The oversampling is a good method overall for addressing the issues that under-sampling faces. However, the major issue of oversampling is overfitting, where the results are too tailored to the input data.