Imbalanced Data in ML: Best Practices for Real-World Projects

Hey there! Have you ever trained a machine learning model that showed 95% accuracy, only to realize later that it completely missed the outcomes that truly matter, like fraud cases or cancer diagnoses? That's the trap of imbalanced data — where one class heavily outweighs the other, silently skewing your model’s performance.

Imbalanced data is everywhere in the real world, from healthcare and finance to cybersecurity. If not handled properly, it can lead your models to make biased decisions, overlook rare events, and fail in high-stakes applications. In this blog, we will deep dive into what imbalanced data is and how to beat it using practical techniques, and also explore the right evaluation metrics that truly reflect your model’s performance.

What is Imbalanced Data?

In classification problems, imbalanced data refers to a scenario where the distribution of classes is skewed, meaning one class (usually the “negative” class) significantly outnumbers the other (the “positive” or minority class), resulting in unequal class distribution. Let’s say in fraud Detection, we have two classes, i.e., fraudulent and non-fraudulent: If 10% of the instances belong to fraudulent and 90% to non-fraudulent, the dataset is considered imbalanced. In this case, the non-fraudulent class will…