Variable Importance in Prediction (Classification or Regression) Molels

Is there a way to calculate the relative importance of each input variable for a given prediction model? I could not find it in KNIME. Most commercial DM tools like IBM SPSS Modeler, Statistica and SAS EM has this feature. It is not the same as variable selection. Rather, it is derived from the sensitivity analysis of the model, following a leave one out and assess the relative degradation on predictive accuracy methodology.

Thank you.


Hi Delen, 

Have you had a look at the backward feature elimination loop?  I think it will give you the information that you need.  Other nodes, such as the logistic regression and Tree Ensemble have additional parameters produced by the learners that give some hints about variable importance. 



I have not looked at it. To be honest, i do not know how to do it. I am new to KNIME and am very much interested in it. I am still using other tools that provides Variable Importance as part of their output. I honesty think it is among the most informative features of predictive analytics. Without it, you are just predicting. With it, you are also explaining the variable contributions/importance. Do you happen to have a workflow I can look at, or use to create Variable Importance scores?

Thank you!


Hi Delen,

I made you a workflow demonstrating how Variable Importance can be realized using KNIME.

I hope this will get you started. If you need anything else, leave me a note and we will figure it out :)

Best, Iris


How do I import the .zip file into Knime? Thanks

Hi Farmer,

in the quick-start guide you find all information about importing / exporting workflow into KNIME:

I hope this helps,


Thanks Christian - I should have looked there first!


Thank you very much, Iris.  This workflow is very helpful and instructive.