Undersampling
Undersampling is a technique used in data science to balance imbalanced datasets. It involves reducing the number of instances in the majority class to match the number of instances in the minority class. This is done by randomly selecting a subset of the majority class instances. Undersampling is commonly used in binary classification problems where the number of negative instances far outweighs the number of positive instances. By undersampling the negative instances, the resulting dataset is more balanced and the classifier can be trained more effectively. However, undersampling can also result in loss of information and reduced accuracy if the dataset is too small or the minority class is not well represented.
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