I am relatively new to some machine learning techniques such as cross-validation alongside being quite new to R programming.
However, I am interested in replicating an out-of-sample technique used by Hothorn & Zeileis (2020).
In particular, Supplementary Manual: Section 3.3 (Figure 15) is said to be generated using an: "out-of-sample (50 times 4:1 subsampling) approach.'
I wondered if someone could post some code on how to do this type of '50 times 4:1' type cross-validation on a toy data set?
The estimation methods and performance metrics are irrelevant. Would just love to see some type of process for replicating the graph and the '50 times in 4:1' cross-validation.
Would be genuinely appreciated.