Class Imbalance

Class imbalance refers to a situation in which the classes in the target variable are not represented equally in a dataset. In other words, one class has significantly more instances than the other class(es). This can cause problems in machine learning models as they tend to be biased towards the majority class, leading to poor performance on the minority class. Class imbalance is a common problem in many real-world applications such as fraud detection, medical diagnosis, and anomaly detection. To address this issue, various techniques can be used such as resampling methods (oversampling or undersampling), cost-sensitive learning, and ensemble methods. Resampling methods involve either increasing the number of instances in the minority class (oversampling) or decreasing the number of instances in the majority class (undersampling). Cost-sensitive learning involves assigning different misclassification costs to different classes. Ensemble methods combine multiple models to improve the performance on the minority class.