# Issue found in feature weight of RandomForest for regression

marcin_blachnik
Member Posts:

**61**Guru
It seems that there is an issue or a bug in the feature_weights returned by RandomForest operator, but only for regression. I found that problem on one dataset but I reconstructed it on IRIS dataset for which features a3 and a4 are the most important but according to the regression RandomForest these two features are the least important.

I evaluated other implementations of RandomForest for regression which returns correct weights (weights which are expected).

Best regards

Marcin

I evaluated other implementations of RandomForest for regression which returns correct weights (weights which are expected).

Best regards

Marcin

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## Answers

1,635UnicornLindon Ventures

Data Science Consulting from Certified RapidMiner Experts

61GuruBelow I attach another process where it can be seen that the attribute with pure noise is the second most important variable according to RapidMiner implementation of RandomForest (the most important also seems to be attribute selected by chance). Because the trees are simple (5 trees of depth 5) one can count how many times each attribute appeared as a decision node. The noise variable is the least important.

3,365RM Data Scientist~Martin

Dortmund, Germany

61Guru24RM Engineering