Decision tree prediction - Individual driving factors

Dear community,

I was applying a Decision Tree model (learner and predictor) to perform a turnover prediction for employees in a company. After looking at the outputs including a probability for an individual to leave/stay we were asking ourselves, if there is a possibility to identify the “driving factors” per individual case. By generating this information, we would then be able to better understand which actions could be taken (based on an individual level) to mitigate certain probabilities.

Any ideas how this can be achieved in KNIME?

Thanks and best regards,

Marcel