Data Balancing

Data balancing is the process of adjusting the distribution of classes in a dataset to address class imbalance issues.

Data balancing is commonly used in machine learning to improve model performance, especially in classification tasks where some classes are underrepresented compared to others. Class imbalance can lead to biased models that perform poorly on the minority class.

Data balancing works by either oversampling the minority class, undersampling the majority class, or using synthetic data generation techniques to create a balanced dataset.

For example, Synthetic Minority Oversampling Technique (SMOTE) is a popular data balancing method that generates synthetic samples for the minority class by interpolating between existing minority samples. This helps to create a more balanced dataset without simply duplicating existing samples.

Another example is oversampling, where the minority class is randomly duplicated until the classes are balanced. Undersampling, on the other hand, involves randomly removing samples from the majority class to achieve balance.

Data balancing is an essential technique in machine learning to build fair and accurate models by addressing class imbalance issues.