Solving The Class Imbalance Problem

Imbalanced classification is a common problem in machine learning, particularly in the realm of binary classification. This occurs when the training dataset has an unequal distribution of classes, leading to a potential bias in the trained model. Examples of imbalanced classification problems include fraud detection, claim prediction, default prediction, churn prediction, spam detection, anomaly detection, and outlier detection. It is important to address the class imbalance in order to improve the performance of our model and ensure its accuracy. Notice that most, if not all, of the examples, are likely binary classification problems.