Comparing Rapidminer decision tree and Weka's

PireheloPirehelo MemberPosts:12Contributor I
edited December 2018 inHelp

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

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  • PireheloPirehelo MemberPosts:12Contributor 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?

    Thanks,

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

    所以整个RapidMine源代码里r Studio Core ison GitHub. Have at it.:)

    Scott

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