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[Solved]: How does the ID3 Algorithm differ from a generic Decision Tree learning algorithm

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Based on the notes of my Machine Learning lecturer, I am struggling to understand how the two algorithms differ? Both seem to select the most informative feature A (based on least entropy), then create subsets of the example set for each value of A. Then create a new branch on the tree by recursively calling ID3 on each subset. Could someone please explain how they differ?

Asked By : Sammdahamm

Answered By : D.W.

The ID3 algorithm is an algorithm for inferring a decision tree: given a training set, it tries to find a decision tree that tends to label a large fraction of the training set accurately. See, e.g.,

There is no such thing as "the generic decision tree learning algorithm". There is no such generic algorithm.

There are many algorithms for learning a decision tree. ID3 is probably the most well-known algorithm for decision tree learning, and the simplest, but there are other algorithms. Other algorithms include C4.5, CART, and others.

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