Random Oversampling and Undersampling for Imbalanced Classification
Last Updated on August 28, 2020 Imbalanced datasets are those where there is a severe skew in the class distribution, such as 1:100 or 1:1000 examples in the minority class to the majority class. This bias in the training dataset can influence many machine learning algorithms, leading some to ignore the minority class entirely. This is a problem as it is typically the minority class on which predictions are most important. One approach to addressing the problem of class imbalance […]
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