Cross Validation and Bootstrapping are resampling methods primarily employed for assessing test-set predictions. Cross Validation is utilized to estimate the test-set prediction error, while Bootstrapping helps determine standard deviation and bias parameters. Together, the combination of bias and variance from Bootstrapping contributes to understanding the overall prediction error.
In addition to Cross Validation and Bootstrapping, other techniques such as validation sets are often employed to achieve the best possible model performance. In the validation set approach, the dataset is randomly divided into two halves: a training set and a validation set. The training data is used to train the model, and then its predictive performance is evaluated on the validation set. This assessment is typically based on metrics like mean square error, providing an estimation of the model’s test error.
K-fold cross-validation represents a specific variant of Cross Validation. In K-fold cross-validation, the dataset is partitioned into “k” parts or folds, allowing the model to be trained and tested “k” times, with each fold serving as a test set once. This technique offers a robust means to assess model performance by mitigating issues such as overfitting and providing a more comprehensive evaluation of how well the model generalizes to unseen data.