Model Evaluation 편집하기 (부분)

==Model Evaluation Techniques==
Several techniques are used to evaluate models effectively:
===Holdout Method===
*Split the dataset into training, validation, and testing sets.
*Train the model on the training set, tune hyperparameters on the validation set, and evaluate performance on the testing set.
===Cross-Validation===
*Partition the dataset into \( k \) folds and perform \( k \)-fold cross-validation.
*Each fold serves as a testing set once, and the remaining \( k-1 \) folds are used for training.
===Bootstrapping===
*Randomly resample the dataset with replacement and evaluate the model on each resampled set.
===Leave-One-Out Cross-Validation (LOOCV)===
*Use all but one data point for training and test on the single data point. Repeat for every data point.