Feature Importance using decision tree

[ipazin]

Hello @sschacht,

see these two topics:

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. …

Feature Importance in Decision Trees

Feature importance is difficult to discern from all of KNIME’s tree nodes. For global feature importance, Random Forest and Tree Ensemble provide split information for 3 levels - but if trees are deeper that split information is lost (a frustration already expressed by @aconca at Tree Ensemble Learner - variable importance?). For H2O GBT, feature importance is provided as a single score. But the KNIME GBT nodes provide no indicators of feature importance. There is also no means to extract the tr…

Br,
Ivan