ID3 algorithm - Revision history

BarFriedrich: Fixed missing period.

2024-07-01T18:04:46Z

Fixed missing period.

← Previous revision		Revision as of 18:04, 1 July 2024
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	{{Short description\|Decision tree algorithm}}		{{Short description\|Decision tree algorithm}}
	[[File:ID3 algorithm decision tree.svg\|thumb\|Potential ID3-generated decision tree. Attributes are arranged as nodes by ability to classify examples. Values of attributes are represented by branches.]]		[[File:ID3 algorithm decision tree.svg\|thumb\|Potential ID3-generated decision tree. Attributes are arranged as nodes by ability to classify examples. Values of attributes are represented by branches.]]
	In [[decision tree learning]], '''ID3''' ('''Iterative Dichotomiser 3''') is an [[algorithm]] invented by [[Ross Quinlan]]<ref>Quinlan, J. R. 1986. Induction of Decision Trees. Mach. Learn. 1, 1 (Mar. 1986), 81–106</ref> used to generate a [[decision tree learning\|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]] ~~domain~~		In [[decision tree learning]], '''ID3''' ('''Iterative Dichotomiser 3''') is an [[algorithm]] invented by [[Ross Quinlan]]<ref>Quinlan, J. R. 1986. Induction of Decision Trees. Mach. Learn. 1, 1 (Mar. 1986), 81–106</ref> used to generate a [[decision tree learning\|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.
	==Algorithm==		==Algorithm==
	The ID3 algorithm begins with the original set <math>S</math> as the [[root node]]. On each [[iteration]] of the algorithm, it iterates through every unused [[Feature (machine learning)\|attribute]] of the set <math>S</math> and calculates the [[Entropy (information theory)\|entropy]] <math>\Eta{(S)}</math> or the [[Information gain in decision trees\|information gain]] <math>IG(S)</math> of that attribute. It then selects the attribute which has the smallest entropy (or largest information gain) value. The set <math>S</math> is then split or [[Partition of a set\|partitioned]] by the selected attribute to produce subsets of the data. (For example, a node can be split into [[child node]]s based upon the subsets of the population whose ages are less than 50, between 50 and 100, and greater than 100.) The algorithm continues to [[Recursion (computer science)\|recurse]] on each subset, considering only attributes never selected before.		The ID3 algorithm begins with the original set <math>S</math> as the [[root node]]. On each [[iteration]] of the algorithm, it iterates through every unused [[Feature (machine learning)\|attribute]] of the set <math>S</math> and calculates the [[Entropy (information theory)\|entropy]] <math>\Eta{(S)}</math> or the [[Information gain in decision trees\|information gain]] <math>IG(S)</math> of that attribute. It then selects the attribute which has the smallest entropy (or largest information gain) value. The set <math>S</math> is then split or [[Partition of a set\|partitioned]] by the selected attribute to produce subsets of the data. (For example, a node can be split into [[child node]]s based upon the subsets of the population whose ages are less than 50, between 50 and 100, and greater than 100.) The algorithm continues to [[Recursion (computer science)\|recurse]] on each subset, considering only attributes never selected before.

Ixfd64: vector version

2024-01-14T22:17:21Z

vector version

← Previous revision		Revision as of 22:17, 14 January 2024
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	{{Short description\|Decision tree algorithm}}[[File:ID3 algorithm decision tree.~~png~~\|thumb\|Potential ID3-generated decision tree. Attributes are arranged as nodes by ability to classify examples. Values of attributes are represented by branches.]]		{{Short description\|Decision tree algorithm}}
			[[File:ID3 algorithm decision tree.svg\|thumb\|Potential ID3-generated decision tree. Attributes are arranged as nodes by ability to classify examples. Values of attributes are represented by branches.]]
	In [[decision tree learning]], '''ID3''' ('''Iterative Dichotomiser 3''') is an [[algorithm]] invented by [[Ross Quinlan]]<ref>Quinlan, J. R. 1986. Induction of Decision Trees. Mach. Learn. 1, 1 (Mar. 1986), 81–106</ref> used to generate a [[decision tree learning\|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]] domain		In [[decision tree learning]], '''ID3''' ('''Iterative Dichotomiser 3''') is an [[algorithm]] invented by [[Ross Quinlan]]<ref>Quinlan, J. R. 1986. Induction of Decision Trees. Mach. Learn. 1, 1 (Mar. 1986), 81–106</ref> used to generate a [[decision tree learning\|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]] domain
	==Algorithm==		==Algorithm==

Whpq: Copyvio revdel completed (RR)

2023-11-30T01:44:38Z

Copyvio revdel completed (RR)

← Previous revision		Revision as of 01:44, 30 November 2023
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	{{Short description\|Decision tree algorithm}}{{Copyvio-revdel\|url=Mitchell, Tom Michael (1997). Machine Learning. New York, NY: McGraw-Hill. p. 56. ISBN 0070428077. OCLC 36417892.\|start=29886554\|comment=The pseudocode that was provided here before had been copied verbatim from the textbook given above, a physical copy of which I have consulted in my library. This was also pointed out on the talk page a long time ago but apparently missed\|end=1172193618}}[[File:ID3 algorithm decision tree.png\|thumb\|Potential ID3-generated decision tree. Attributes are arranged as nodes by ability to classify examples. Values of attributes are represented by branches.]]		{{Short description\|Decision tree algorithm}}[[File:ID3 algorithm decision tree.png\|thumb\|Potential ID3-generated decision tree. Attributes are arranged as nodes by ability to classify examples. Values of attributes are represented by branches.]]
	In [[decision tree learning]], '''ID3''' ('''Iterative Dichotomiser 3''') is an [[algorithm]] invented by [[Ross Quinlan]]<ref>Quinlan, J. R. 1986. Induction of Decision Trees. Mach. Learn. 1, 1 (Mar. 1986), 81–106</ref> used to generate a [[decision tree learning\|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]] domain		In [[decision tree learning]], '''ID3''' ('''Iterative Dichotomiser 3''') is an [[algorithm]] invented by [[Ross Quinlan]]<ref>Quinlan, J. R. 1986. Induction of Decision Trees. Mach. Learn. 1, 1 (Mar. 1986), 81–106</ref> used to generate a [[decision tree learning\|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]] domain
	==Algorithm==		==Algorithm==

Felix QW: Remove pseudocode as copyvio

2023-11-29T16:23:17Z

Remove pseudocode as copyvio

Can't load revision 1187494888

212.124.163.71: Undid revision 1145828209 by 182.76.193.198 (talk)

2023-08-25T15:16:30Z

Undid revision 1145828209 by 182.76.193.198 (talk)

Can't load revision 1172193618

103.119.241.98 at 09:37, 15 June 2023

2023-06-15T09:37:22Z

Can't load revision 1160251660

182.76.193.198: /* Pseudocode */

2023-03-21T06:22:09Z

Pseudocode

Can't load revision 1145828209

I dream of horses: Autowikibrowser cleanup, typo(s) fixed: time consuming → time-consuming

2022-09-19T06:29:52Z

Autowikibrowser cleanup, typo(s) fixed: time consuming → time-consuming

Can't load revision 1111083600

ChromeGames: Importing Wikidata short description: "Decision tree algorithm" (Shortdesc helper)

2022-04-16T08:33:20Z

Importing Wikidata short description: "Decision tree algorithm" (Shortdesc helper)

Can't load revision 1082985568

2409:4071:D9D:45A0:D611:1C28:C71C:2D48 at 19:06, 19 January 2022

2022-01-19T19:06:34Z

Can't load revision 1066714179

← Previous revision		Revision as of 18:04, 1 July 2024
Line 1:		Line 1:
	{{Short description\|Decision tree algorithm}}		{{Short description\|Decision tree algorithm}}
	[[File:ID3 algorithm decision tree.svg\|thumb\|Potential ID3-generated decision tree. Attributes are arranged as nodes by ability to classify examples. Values of attributes are represented by branches.]]		[[File:ID3 algorithm decision tree.svg\|thumb\|Potential ID3-generated decision tree. Attributes are arranged as nodes by ability to classify examples. Values of attributes are represented by branches.]]
	In [[decision tree learning]], '''ID3''' ('''Iterative Dichotomiser 3''') is an [[algorithm]] invented by [[Ross Quinlan]]<ref>Quinlan, J. R. 1986. Induction of Decision Trees. Mach. Learn. 1, 1 (Mar. 1986), 81–106</ref> used to generate a [[decision tree learning\|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]] ~~domain~~		In [[decision tree learning]], '''ID3''' ('''Iterative Dichotomiser 3''') is an [[algorithm]] invented by [[Ross Quinlan]]<ref>Quinlan, J. R. 1986. Induction of Decision Trees. Mach. Learn. 1, 1 (Mar. 1986), 81–106</ref> used to generate a [[decision tree learning\|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.
	==Algorithm==		==Algorithm==
	The ID3 algorithm begins with the original set <math>S</math> as the [[root node]]. On each [[iteration]] of the algorithm, it iterates through every unused [[Feature (machine learning)\|attribute]] of the set <math>S</math> and calculates the [[Entropy (information theory)\|entropy]] <math>\Eta{(S)}</math> or the [[Information gain in decision trees\|information gain]] <math>IG(S)</math> of that attribute. It then selects the attribute which has the smallest entropy (or largest information gain) value. The set <math>S</math> is then split or [[Partition of a set\|partitioned]] by the selected attribute to produce subsets of the data. (For example, a node can be split into [[child node]]s based upon the subsets of the population whose ages are less than 50, between 50 and 100, and greater than 100.) The algorithm continues to [[Recursion (computer science)\|recurse]] on each subset, considering only attributes never selected before.		The ID3 algorithm begins with the original set <math>S</math> as the [[root node]]. On each [[iteration]] of the algorithm, it iterates through every unused [[Feature (machine learning)\|attribute]] of the set <math>S</math> and calculates the [[Entropy (information theory)\|entropy]] <math>\Eta{(S)}</math> or the [[Information gain in decision trees\|information gain]] <math>IG(S)</math> of that attribute. It then selects the attribute which has the smallest entropy (or largest information gain) value. The set <math>S</math> is then split or [[Partition of a set\|partitioned]] by the selected attribute to produce subsets of the data. (For example, a node can be split into [[child node]]s based upon the subsets of the population whose ages are less than 50, between 50 and 100, and greater than 100.) The algorithm continues to [[Recursion (computer science)\|recurse]] on each subset, considering only attributes never selected before.