Cross-Validation: 편집 역사

2024년 11월 5일 (화)

• 최신이전 02:282024년 11월 5일 (화) 02:28‎ 핵톤 토론 기여‎ 4,467 바이트 +4,467‎ Created page with "Cross-Validation is a technique in machine learning used to evaluate a model’s performance on unseen data. It involves partitioning the dataset into multiple subsets, training the model on some subsets while testing on others. Cross-validation helps detect overfitting and underfitting, ensuring the model generalizes well to new data. ==Key Concepts in Cross-Validation== Cross-validation is based on the following key principles: *'''Training and Validation Splits''': Cr..." 태그: 시각 편집