Hello all, I am interested to conduct a k-fold validation for an algorithm that uses TDB as its database. The stored graph is weighted based on some criteria. The point is that when performing k-fold cross validation I have for each iteration (k-times) to create the TDP repo, to load the training models, to weight the graph, calculate the Precision of the algorithm with the remaining test models, delete the complete graph again, and so it iterates for each step.
My question is if I have to completely delete for each time all the files and create a new dataset for each iteration? Or, is there maybe any other more appropriate way to perform k-fold cross-validation with a TDB? Thanks.
