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I am doing research on data mining and more precisely, decision trees.

I would like to know if there are multiple algorithms to build a decision tree (or just one?), and which is better, based on criteria such as

  • Performance

  • Complexity

  • Errors in decision making

  • and more.

1 Answer

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by (33.1k points)

Decision Tree implementations differ along these axes:

  • Splitting criterion 

  • Training model for regression (continuous variables) as well as classification (discrete variables)

  • Technique to reduce over-fitting

  • whether it can handle incomplete data or not

The major Decision Tree implementations are:

  • ID3, or Iterative Dichotomizer, was the first of three Decision Tree implementations

  • CART, or Classification And Regression Trees is often used as a generic acronym for the term Decision Tree

  • C4.5 is used commonly because it:

  1. accepts both continuous and discrete features

  2. handles incomplete data points

  3. solves over-fitting problem

Hope this answer helps.

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