Methods for Transforming Imbalanced Data into Balanced Data

Understand the methods used to deal with imbalanced data.

Addressing class imbalance is essential for improving the performance of ML models, particularly in situations where one class has a significantly higher number of examples than another. In this lesson, we’ll explore the methods commonly used to transform imbalanced data into balanced data.


The oversampling method is used to correct the imbalance in the dataset by creating additional instances of the minority class so that its instances becomes closer in number to that of the majority class. This helps the ML model learn from both classes equally. It’s important to note that oversampling is the only viable solution to balancing the dataset when collecting further data from the minority class is feasible and practical.

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