Most machine learning algorithms assume there are equal numbers of examples for each class in the source data. Many datasets contain substantially different numbers of records for important classes — resulting in an imbalanced class problem. Failure to handle this properly results in models with poor predictive performance.
Knowledge Studio has a node specifically built to handle imbalanced class issues. In this video, you will learn how to identify an imbalanced class problem and use the software’s Handle Class Imbalance node to correct it.
Refer to the Imbalanced-Learn Documentation website to learn more about the challenges related to working with imbalanced classes