FLEX-SMOTE: Synthetic over-sampling technique that flexibly adjusts to different minority class distributions

THE BIGGER PICTURE Machine learning methods often encounter imbalanced classification problems, particularly when dealing with binary classification. This occurs when the distribution of classes in the training dataset is uneven, which can lead to bias in the trained model. Fraud detection, claim prediction, default prediction, spam filtering, disease screening, churn prediction, anomaly detection, and outlier identification tasks are a few examples of imbalanced classification issues. To enhance the performance and ensure the accuracy of a model, it is crucial to address the issue of class imbalance. Predictive modeling is complicated by imbalanced datasets, but this is to be expected, as the real world consists of biased cases. By preventing the dataset from becoming biased toward one class, balancing it makes it easier to train a model. To put it another way, the model will not continue to favor the majority class solely based on having more data.