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does RM still uses the C4.5 algorithm for his decision tree operator? I found some really old topics that say it uses the C4.5 algorithm.
The shipped descion tree is actually a own implemtation. I am not sure if it follows any direct implementation, i think it is some mixture, but I am not sure.
If you want to have C4.5 you may have a look on W-J48.
Edit: Just looked it up:
* <p> * This operator learns decision trees from both nominal and numerical data. Decision trees are * powerful classification methods which often can also easily be understood. This decision tree * learner works similar to Quinlan's C4.5 or CART. * </p> * * <p> * The actual type of the tree is determined by the criterion, e.g. using gain_ratio or Gini for * CART / C4.5. * </p>
thank your very much for your help! But where did you found the information? Is it from a public source which I dond know? I can not find the information in operators manual.
i copied it from the source code. It is available at https://github.com/rapidminer/rapidminer-studio/blob/master/src/main/java/com/rapidminer/operator/learner/tree/ParallelDecisionTreeLearner.java . Im a bit unsure where this comment pops up in studio.
If you get the Weka extension (which is free and highly recommended, since it has over a hundred predictive modeling operators) then you will have access to several different flavors of decision tree operators, including the weka implementation of the C4.5 algorithm in the W-J48 operator.
so I guess the C5.0 is not yet implemented in Rapidminer?
Where can i find C5 in Rapid Miner? It's not in the decision tree section?
You'll need to download the Weka extension to get it.
but I think C5.0 is too new and still under license to be implemented in rapidminer or weka yet, is that true?
its only C4.5 in Weka I think...
I am Thathar
shall I use C4.5 algorithm in Rapidminer?
hello @thatharpandi199 - I would use the normal Decision Tree operator in Studio Core. It is the best by far.