From Open Risk Manual


Cross-Validation is an approach to empirical Model Validation of statistically based risk models using historical data.

Validation Set Approach

The simplest validation set approach involves dividing the available data into a training set or Development Sample and a validation set or Hold-Out Sample

Leave-One-Out Cross-Validation

This approach involves splitting the data set repeatedly into two parts: Instead of creating two subsets of roughly comparable size, a single data point is used for validation and the remaining data make up the training set.

k-Fold Cross-Validation

Similar to the Leave-One-Out approach but instead of a single data point, the sample is split into k roughly equally sized folds and in each iteration one of them is used for validation

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