random forest weight calculation method in rapidminer node

New Altair Community Member
Updated by Jocelyn
I am studying feature importance. A question that arises while studying is that python sklearn random forest calculates feature importance through MDI, and I want to know how the random forest in rapidminer calculates weights.
1. MDI(Mean Decrease in Impurity) Importance
2. Permutation Importance
3. Drop Column Importance
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@yyhuang
https://towardsdatascience.com/the-mathematics-of-decision-trees-random-forest-and-feature-importance-in-scikit-learn-and-spark-f2861df67e3
Could you please tell me if it is like the spark random forest feature importance method in this article?



https://towardsdatascience.com/the-mathematics-of-decision-trees-random-forest-and-feature-importance-in-scikit-learn-and-spark-f2861df67e3
Could you please tell me if it is like the spark random forest feature importance method in this article?

To understand the attribute weights calculated for Random Forest in RapidMiner, please refer to the open sourced GitHub page.
According to the documentation, the output port for feature weight from RF model returns the
Hope it helps!
YY