ID3 Algorithm Amrit Gurung. Classification Library System Organise according to special characteristics Faster retrieval New items sorted easily Related.

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Presentation transcript:

ID3 Algorithm Amrit Gurung

Classification Library System Organise according to special characteristics Faster retrieval New items sorted easily Related items clustered together

ID3 Algorithm Ross Quinlan Mathematical algorithm Decision tree Fixed examples Entropy Gain

ID3 Algorithm Entropy (randomness) Entropy(S) = S -p(I) log2 p(I) Range 0-1 Gain: Attribute property Gain(S, A) = Entropy(S) - S ((|S v | / |S|) * Entropy(S v ))

ID3 Algorithm Example NameHairHeightWeightLotionResult Sarahblondeaveragelightnosunburned Danablondetallaverageyesnone Alexbrownshortaverageyesnone Annieblondeshortaveragenosunburned Emilyredaverageheavynosunburned Petebrowntallheavynonone Johnbrownaverageheavynonone Katieblondeshortlightyesnone

ID3 Algorithm Example AttributeEntropy Hair Color0.50 Height0.69 Weight0.94 Lotion0.61

ID3 Algorithm Example AttributeEntropy Height0.50 Weight1.00 Lotion0.00

Limitations of ID3 Algorithm Considers only one attribute to create nodes Numerous trees needed for continuous data Over classification for small data

ID3 Algorithm Advantages Predict new data Training set is used to create rules for predicting

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