Data Preprocessing Questions
Data balancing refers to the process of equalizing the distribution of different classes or categories within a dataset. It involves adjusting the number of instances or samples in each class to ensure that they are represented equally. This is typically done to address class imbalance issues, where one or more classes have significantly fewer instances compared to others. Data balancing techniques aim to improve the performance and accuracy of machine learning models by providing a more balanced and representative dataset for training.