Oversampling: 편집 역사

2024년 11월 5일 (화)

• 최신이전 06:472024년 11월 5일 (화) 06:47‎ 핵톤 토론 기여‎ 5,524 바이트 +5,524‎ Created page with "Oversampling is a technique used in data science and machine learning to address class imbalance by increasing the number of samples in the minority class. In classification tasks with imbalanced datasets, oversampling helps to balance the distribution of classes, allowing the model to learn patterns from both majority and minority classes. Oversampling is commonly used in applications such as fraud detection, medical diagnosis, and other areas where certain classes are..." 태그: 시각 편집