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.
설명	입력하는 내용	문서에 나오는 결과
기울임꼴	''기울인 글씨''	기울인 글씨
굵게	'''굵은 글씨'''	굵은 글씨
굵고 기울인 글씨	'''''굵고 기울인 글씨'''''	*굵고 기울인 글씨*