Comparing Rapidminer decision tree and Weka's
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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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,
Altair Employee
Altair Employee
hello @Pirehelokan - did you search for this thread?
https://community.rapidminer.com/t5/RapidMiner-Studio-Forum/Decision-Tree-Operator-C5-0-or-C4-5-algorithm/m-p/31187#M22488
Scott