Cross-Validation 편집하기 (부분)

==Types of Cross-Validation==
Several types of cross-validation are commonly used, each suited to different datasets and modeling needs:
*'''k-Fold Cross-Validation''': The dataset is divided into k equal-sized folds. The model is trained on k-1 folds and tested on the remaining fold, repeating this process k times and averaging the results.
*'''Stratified k-Fold Cross-Validation''': Similar to k-fold cross-validation, but preserves the distribution of labels across folds, useful for imbalanced datasets.
*'''Leave-One-Out Cross-Validation (LOOCV)''': Each data point serves as its own test set, with the model trained on all other data points. This method is computationally intensive but provides a highly accurate performance estimate.
*'''Holdout Method''': A simpler approach that splits the data into a single training and test set without rotation, useful for large datasets.
*'''Time Series Cross-Validation''': For time-ordered data, this method trains the model on past observations and tests it on future observations, preserving the temporal order.