What is Data Imbalance in Machine Learning?

A software platform for organizations and developers to responsibly deploy, monitor, and get value from AI - at scale. Data imbalance, or imbalanced classes, is a common problem in machine learning classification where the training dataset contains a disproportionate ratio of samples in each class. Examples of real-world scenarios that suffer from class imbalance include threat detection, medical diagnosis, and spam filtering. Class imbalance can make training efficient machine learning models difficult, especially when there aren't enough samples belonging to the class of interest. In the case of fraud detection, the amount of fraudulent transactions is negligible to the number of lawful transactions, making it difficult to train a machine learning model because the training dataset does not contain enough information about fraud.