Is-this-a-good-customer

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