Underfitting: 편집 역사

2024년 11월 5일 (화)

• 최신이전 02:262024년 11월 5일 (화) 02:26‎ 핵톤 토론 기여‎ 4,364 바이트 +4,364‎ Created page with "Underfitting is a common issue in machine learning where a model is too simple to capture the underlying patterns in the data. As a result, the model performs poorly on both training and test datasets, failing to achieve high accuracy. Underfitting occurs when the model lacks the capacity or complexity needed to represent the relationships within the data. ==Causes of Underfitting== Several factors contribute to underfitting in machine learning models: *'''Over-Simplifie..." 태그: 시각 편집