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. Up-sampling minority class refers to the technique of oversampling the under-represented class in a binary classification problem to balance the class distribution. The idea behind up-sampling is to randomly duplicate examples from the minority class to increase its representation in the dataset and make the class distribution more balanced.