Decision Tree Algorithm


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Business Entreprise data Les données Tree Arbre Decision Tree Arbre de décision Simple Decision Tree Arbre de décision simple Decision Décision Attribute Attribut

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database 17 • Class P: buyscomputer = “yes” • Class N: buyscomputer buys computer = “no” no 9 9 5 5 Entropy ( D ) = − log2 ( ) − log2 ( ) =0.940 14 14 14 14 • Compute the expected information requirement for each attribute: start with the attribute age Gain( age, D ) = Entropy ( D ) − Sv Entropy ( Sv ) ∑ v∈ Youth , Middle − aged , Senior S = Entropy ( D ) − 5 4 5 Entropy ( Syouth ) − Entropy ( Smiddle aged ) − Entropy ( Ssenior ) 14 14 14 = 0.246 Gain (income, D ) = 0.029 Gain ( student , D ) = 0.151 Gain ( credit rating , D ) = 0.048 18 Figure 6.5 The attribute age has the highest information gain and therefore becomes the splitting attribute at the root node of the decision tree..


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