How to Handle Imbalanced Classes in Machine Learning

Imbalanced classes put "accuracy" out of business. This is a surprisingly common problem in machine learning (specifically in classification), occurring in datasets with a disproportionate ratio of observations in each class. Standard accuracy no longer reliably measures performance, which makes model training much trickier. In this guide, we'll explore 5 effective ways to handle imbalanced classes. Let's say your client is a leading research hospitals, and they've asked you to train a model for detecting a disease based on biological inputs collected from patients.