I am using the Weka GUI for classifying sensor data.
I have measures of 10 people, the data is sorted. So the first 10% correspond to participant 1, the second 10% to participant 2 etc.
I would like to use 10 fold cross validation to build a model on 9 participants and test it on the remaining participant. In my case I believe I could accomplish this by simply not randomizing the data splits.
How would I best go about doing this?
I don't know how to do this in the Explorer.
In the KnowledgeFlow GUI, there is a CrossValidationFoldMaker used to create cross-validation folds. This has an option to Preserve instances order, which says it preserves the order of instances rather than randomly shuffling.
There's a video describing the KnowledgeFlow interface here: https://www.youtube.com/watch?v=sHSgoVX9z-8&t=7s