In under-sampling, instead of using all observations of the majority class to train the model, only a random subset of the majority class is used in addition to the minority class. Training samples of the majority class are randomly eliminated until the ratio of the majority and minority classes reach a preset value, usually close to 1. A disadvantage of under-sampling is that it reduces the data available for training. In over-sampling, training samples of the minority class is over-sampled at random until the relative size of the minority and majority classes is more balanced. Note that over-sampling may.
