imbalancedclassification

Imbalanced classification represents one of the most pervasive challenges in machine learning, where the distribution of classes in training data is heavily skewed. Whether you’re detecting fraudulent transactions, diagnosing rare diseases, or identifying network intrusions, the minority class—often the one you care about most—may represent only 1-5% of your dataset. Traditional classification approaches fail catastrophically … Read more