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This document provides an overview of existing solutions for dealing with imbalanced datasets in machine learning. It discusses challenges that traditional classification algorithms face when applied to imbalanced datasets where the classes are not equally represented. Common techniques discussed to address imbalanced datasets include oversampling the minority class, undersampling the majority class, and algorithmic approaches like cost-sensitive learning and modified support vector machines (SVMs). The doc

Author

fecol32501

Language

EN