I am trying to find a simple way to evaluate variable importance in a predictive model. I found this topic on the forum that give an example: https://tech.knime.org/forum/knime-general/variable-importance-in-prediction-classification-or-regression-molels

Unfortunately, I don't really understand what the workflow is doing and why. Is there anyone who can give me a short explanation. What is the purpose of the target shuffling? And how can I evaluate which variables do matter and which variables are less important?

But, for a regression based model this is only true when the variables are independent, orthogonal, not when they are correlated. Otherwise, the joint effect(s) is not taken into account.

I'm afraid that unless the variables are independent (here meaning all correlations=0), it's not possible to isolate the contribution of each variable to the total variability( variance).