Dealing with imbalanced datasets in MATLAB
Dealing with imbalanced datasets is a common challenge in machine learning, particularly in classification tasks where one class significantly outnumbers the other(s). Handling imbalanced datasets is crucial because models trained on such data may exhibit bias toward the majority class and perform poorly in predicting the minority class.
Understanding oversampling
Oversampling is a method that’s employed to tackle class imbalance in a dataset by augmenting the number of instances belonging to the minority class. The aim is to balance the class distribution and prevent machine learning models from being biased toward the majority class. Oversampling is particularly useful when you have limited data for the minority class. There are several methods for oversampling, including the following:
- Random oversampling: In random oversampling, you randomly select and duplicate instances from the minority class until the class distribution...
