Multidimensional analysis and detection of informative features in human brain white matter
Fig 5
We evaluate model quality using a nested k-fold cross validation scheme. At level-0, the input data is decomposed into k0 shuffled groups and optimal hyperparameters are found for the level-0 training set. To avoid overfitting, the optimal hyperparameters are themselves evaluated using a cross-validation scheme taking place at level-1 of the decomposition, where each level-0 training set is further decomposed into k1 = 3 shuffled groups. In the classification case, the training and test splits are stratified by diagnosis. For the ALS and WH data, k0 = 10, while for the HBN and Cam-CAN data, k0 = 5.