N-Fold Cross-Validation 편집하기 (부분)

==Importance of N-Fold Cross-Validation==
N-fold cross-validation offers several advantages in model evaluation:
*'''Improved Reliability''': By using multiple folds, cross-validation provides a more robust estimate of model performance compared to a single train-test split.
*'''Reduces Overfitting''': The model is evaluated on multiple subsets of data, which reduces the risk of overfitting by ensuring that the performance estimate is not overly influenced by any single fold.
*'''Maximizes Data Utilization''': Every data point is used in both training and testing, ensuring that the model benefits from all available data for evaluation.