Decision Tree Operator - C5 or 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.

~Martin

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>

Hi Martin,

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.

Kind regards,

Christopher

Dear Christopher,

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.

~Martin

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?

Hi

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.

Scott