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There are many different widely used tree models. This is my notes and understanding of them. Could anyone tell me

  • If I am correct on the model I mentioned
  • If I am missing other "popular" tree model

Statistic community (many packages can be found in R)

  • CART (Classification And Regression Trees). This is from the classical book Classification and Regression Trees by Breiman. This is trademakred name of particular software implementations of the ideas.
  • Tree package in R. Tree has been used for S Plus routines of Clark and Pregibon.
  • RPART package in R. Free version of CART, and widely used in R, because of free it is even more popular than CART
  • PARTY. Conditional Inference Tree Model

Data mining community (many packages can be found in Java)

  • ID3 (Iterative Dichotomiser 3), Invented by Ross Quinlan. ID3 is the precursor to the C4.5 algorithm, and is typically used in the machine learning and natural language processing domains.
  • C45, is an extension of ID3 that accounts for unavailable values, continuous attribute value ranges, pruning of decision trees, rule derivation, and so on.
  • J48, is a re-implementation of C4.5 release 8 (hence the name J48) in Java. A lot of time has been spent getting the same results as the original C4.5. J48 implements both C4.5's confidence-based post-pruning (default) and sub-tree raising.
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CART and ID3 are classical decision tree algorithms. You can also investigate using which measure they are doing splits - there can be Gini index or cross-entropy measuring information gain.

But for practical tasks I think pure trees are rarely used as there are Random Forest or (Gradient) Tree Boosting algorithms showing greater performance, latter uses "dumb" decision stumps which are short trees with a very small number of splits (only 1 or 2 for example).

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