Hands-On Machine Learning for Algorithmic Trading

Leave-one-out CV

The original CV implementation used a leave-one-out method that used each observation once as the validation set, as shown in the following code:

loo = LeaveOneOut()for train, validate in loo.split(data):    print(train, validate)[1 2 3 4 5 6 7 8 9] [0][0 2 3 4 5 6 7 8 9] [1]...[0 1 2 3 4 5 6 7 9] [8][0 1 2 3 4 5 6 7 8] [9]

This maximizes the number of models that are trained, which increases computational costs. While the validation sets do not overlap, the overlap of training sets is maximized, driving up the correlation of models and their prediction errors. As a result, the variance of the prediction error is higher for a model with a larger number of folds.