Robustness of an algorithm

KNIME Analytics Platform
#1

Hello everybody,

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.

Vezio

#2

Hi,

you can use a cross validation loop. An example in the workflow in attachment.

Fabien

#3

Hello,

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?

Thanks

Vezio

#4

You could also try bootstrap sampling.

#5

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.

Fabien