Most real-world classification problems are imbalanced. Fraud, disease, churn, and defects are rare by nature. Standard classifiers chase accuracy, so they quietly ignore the very class you care about. For years, SMOTE was the reflex fix that everyone reached for first. But SMOTE often fails on the messy, high-dimensional data that production systems actually see. […]The post Handling Imbalanced Classification: What Works Better Than SMOTE appeared first on Analytics Vidhya.