Context
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.
Content
Standard accuracy no longer reliably measures performance, which makes model training much trickier.
Imbalanced classes appear in many domains, including:
Antifraud
Antispam
Inspiration
5 tactics for handling imbalanced classes in machine learning:
Up-sample the minority class
Down-sample the majority class
Change your performance metric
Penalize algorithms (cost-sensitive training)
Use tree-based algorithms