I'm new to knime and I have to learn and use it for train an algorithm and use it to make previsions, most probably I'll have to use a decision tree.
My problem is that I have also to demonstrate in some way the robustness of that algorithm so it can be trustable.
Is there an authomatic way inside Knime for doing such a thing? If not, do you have any suggestion?
Thanks in advance and sorry if it is this thread is in the wrong place.
you can use a cross validation loop. An example in the workflow in attachment.
if I've understood the example the way is simply to divide the data set and use a part for train the algorithm and a part for the predictor and then compare the results. Is that right?
You could also try bootstrap sampling.
Hi, yes you divides equally your starting sample and process many train/test. Then you look at the variability of the results for each model.