Introducing imbalanced data

In machine learning, we often come across datasets that need to be more balanced. But what does it mean for a dataset to be imbalanced?

An imbalanced dataset is one where the distribution of samples across the different classes is not uniform. In other words, one type has significantly more samples than the other(s). This is a common scenario in many real-world applications. For instance, in a dataset for fraud detection, the number of non-fraudulent transactions (majority class) is typically much higher than the number of fraudulent ones (minority class).

Imagine a medical dataset recording instances of a rare disease. Most patients will be disease-free, resulting in a large class of healthy records, while only a tiny fraction will be affected by the disease. This disproportion in the distribution of categories is what we call imbalanced data.

Imbalanced data can lead to a significant challenge in predictive modeling. By their very nature, machine...