Feature Importance using decision tree

[sschacht]

Hi Everybody,

I have a question is it possible to extract feature importance in Knime using decision tree learner and predictor?

Using sklearn I usual use the following code for it:

from sklearn.tree import DecisionTreeClassifier
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
dt.score(X_test, y_test)

dt.feature_importances_

For sure I could use the python node, but I try to transfer my normal tasks to KNIME without using python.
Does somebody have an idea?

[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

[HansS]

Hi @sschacht
Maybe the Information Gain Calculator helps you to give some insights about the feature importance.
gr. Hans