N-Fold Cross-Validation: 편집 역사

2024년 11월 5일 (화)

• 최신이전 07:052024년 11월 5일 (화) 07:05‎ 핵톤 토론 기여‎ 5,458 바이트 +5,458‎ Created page with "N-Fold Cross-Validation is a technique used in machine learning to evaluate a model's performance by dividing the dataset into multiple subsets, or "folds." In this method, the dataset is split into N equal parts, where the model is trained on N-1 folds and tested on the remaining fold. This process is repeated N times, each time using a different fold as the test set, and the results are averaged to obtain an overall performance estimate. N-fold cross-validation helps t..." 태그: 시각 편집