Comparing Rapidminer decision tree and Weka's

PireheloPirehelo Member Posts: 12 Contributor I
edited December 2018 in Help

Hi everybody 

Can someone please explain how Rapidminer decision tree operator is different from J48 (or W-J48) decision tree in Weka? The accuracy I get from the latter is considerably higher. Weka's documentation clearly mentioned that J48 is based on C4.5 algorithm. How about Rapidminer's? If they are the same why do they give different accuracies for similar parameters? Plus, despite Rapidminer's, Weka's cannot handle missing data point. 


Thank you 

Best Answer


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    PireheloPirehelo Member Posts: 12 Contributor I

    Thank you Scott,


    That thread was pretty helpful. However, I could not see the source code of the decision tree. I understand that rapidminer decision tree could use both information gain and gini index for splitting nodes. But even when I use the information gain its accuracy is different from Weka's C4.5. So, I was curious to see how is the tree implemented?




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    sgenzersgenzer Administrator, Moderator, Employee, RapidMiner Certified Analyst, Community Manager, Member, University Professor, PM Moderator Posts: 2,959 Community Manager

    so the sourcecode for the entire RapidMiner Studio Core is on GitHub. Have at it. :)





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