Imbalanced classification is a type of Supervised Learning in which the input dataset has an uneven distribution of class labels. Example: Fraud detection in bank transactions, where the number of fraud cases is much less than non-fraud cases.
Imbalanced classification is a type of Supervised Learning in which the input dataset has an uneven distribution of class labels. Example: Fraud detection in bank transactions, where the number of fraud cases is much less than non-fraud cases.