Figure 1: Data before SMOTe-ENN Application
Figure 2 shows the data after SMOTe-ENN oversampling. Note that the data on the red line is more densely packed and is now moving in the same direction as the blue line. This symmetry of the lines is caused by the synthetic data points generated by SMOTe-ENN. These data points are not identical to the actual data points but are close enough to be used to train the model 46,47. The resultant effect is a more accurate model, better able to classify the new data points. By artificially generating additional data points, SMOTe-ENN can ensure that the model can learn from the data and generalize to new inputs (Chawla et al., et al., 2002). In this way, SMOTe-ENN can help to improve the performance of ML models on imbalanced datasets46,47.