Oversampling 편집하기 (부분)

==Applications of Oversampling==
Oversampling is widely used in various machine learning applications that involve class imbalance:
*'''Fraud Detection''': Balancing fraudulent and non-fraudulent transaction data to improve the detection of fraud cases.
*'''Medical Diagnosis''': Increasing samples for rare diseases to improve prediction accuracy and ensure the model does not overlook critical cases.
*'''Customer Churn Prediction''': Balancing churn and non-churn classes to better identify patterns in customer behavior.
*'''Anomaly Detection''': Improving detection of rare events or anomalies, where the minority class represents critical but infrequent outcomes.