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Which MATLAB function is the best for building a decision tree with the CART algorithm?

5 vues (au cours des 30 derniers jours)
Ines
Ines le 16 Mai 2012
Hello there, I want to build a tree using the CART Algorithm and so far I found two different (?) functions in the Matlab statistics toolbox for doing this: ClassificationTree.fit and classregtree, so I am wondering which of them is better or whether they are both based on the same principles, but with different application fields?

Réponses (2)

owr
owr le 16 Mai 2012
I believe they are using the same algorithms. "classregtree" has been around for quite some time, "ClassificationTree.fit" is syntax based on a newer object based framework. Note I havent researched this rigorously, just a hunch.
If I were writing new code, I would go with the object based syntax as that will likely get more bells and whistles down the line.

Muhammad Aasem
Muhammad Aasem le 25 Mai 2012
use classregtree because it will be supported in the future. anyway. both will give you same result (treefit is now calling classregtree)
try this
load fisheriris;
t1 = classregtree(meas,species);
t2 = treefit(meas,species);
view(t1);
view(t2);
  1 commentaire
Ines
Ines le 25 Mai 2012
but how can I assess the goodness of the qualification? I would like to take out 20% for validation and use the residual 80% for the training dataset..and then repeat this procedure several times...i think if I use crossval I cannot use a tree from classregtree (since crossval seems to ask for an object/handle (whatever that might be..)

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