This is an old version, view current version.

29 Held-Out Evaluation and Cross-Validation

Held-out evaluation involves splitting a data set into two parts, a training data set and a test data set. The training data is used to estimate the model and the test data is used for evaluation. Held-out evaluation is commonly used to declare winners in predictive modeling competitions such as those run by Kaggle.

Cross-validation involves repeated held-out evaluations performed by partitioning a single data set in different ways. The training/test split can be done either by randomly selecting the test set, or by partitioning the data set into several equally-sized subsets and then using each subset in turn as the test data with the other folds as training data.

Held-out evaluation and cross-validation may involve any kind of predictive statistics, with common choices being the predictive log density on test data, squared error of parameter estimates, or accuracy in a classification task.