Why imbalanced data problems are complex to solve

Addressing imbalanced data is no walk in the park, and here’s why. At the core of the challenge is the nature of conventional machine learning algorithms. These algorithms minimize overall error and are designed with the assumption of balanced class distributions. This becomes problematic when faced with imbalanced datasets, leading to a pronounced bias toward the majority class.

The gravity of this problem becomes evident when we realize that in many scenarios, it’s the minority class that carries more significance. Take fraud detection or medical diagnoses as cases in point. While fraudulent transactions or disease instances might be sparse, their correct identification is paramount. Yet, a model trained on skewed data might often lean toward predicting the majority class, achieving superficially high accuracy but failing its core objective.

To add to the challenge, conventional metrics, such as accuracy, are only...