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In decision tree learning, ID3 (Iterative Dichotomiser 3) is an algorithm invented by Ross Quinlan [1] used to generate a decision tree from a dataset. ID3 is the precursor to the C4.5 algorithm , and is typically used in the machine learning and natural language processing domains.
John Ross Quinlan is a computer science researcher in data mining and decision theory. He has contributed extensively to the development of decision tree algorithms, including inventing the canonical C4.5 and ID3 algorithms. He also contributed to early ILP literature with First Order Inductive Learner (FOIL).
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The problem of learning an optimal decision tree is known to be NP-complete under several aspects of optimality and even for simple concepts. [35] [36] Consequently, practical decision-tree learning algorithms are based on heuristics such as the greedy algorithm where locally optimal decisions are made at each node. Such algorithms cannot ...
Decision trees can also be seen as generative models of induction rules from empirical data. An optimal decision tree is then defined as a tree that accounts for most of the data, while minimizing the number of levels (or "questions"). [8] Several algorithms to generate such optimal trees have been devised, such as ID3/4/5, [9] CLS, ASSISTANT ...
Well known methods of recursive partitioning include Ross Quinlan's ID3 algorithm and its successors, C4.5 and C5.0 and Classification and Regression Trees (CART). Ensemble learning methods such as Random Forests help to overcome a common criticism of these methods – their vulnerability to overfitting of the data – by employing different ...
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The first version incorporated decision trees (ID3), and neural networks (backprop), which could both be trained without underlying knowledge of how those techniques worked. IBM SPSS Modeler was originally named Clementine by its creators, Integral Solutions Limited. This name continued for a while after SPSS's acquisition of the product.