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
Errors in decision making
Decision Tree implementations differ along these axes:
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:
accepts both continuous and discrete features
handles incomplete data points
solves over-fitting problem
Hope this answer helps.
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