1. Machine Learning Reading: Chapter 18

3. What does it mean to learn?

18. Data Verginica 1.7 2.6 3 2 3 Versicolour 2.1 5.6 4.2 6 9 Verginica 2.2 2.8 3.7 1 8 Setosa 1.2 1.6 4.3 6.3 7 Setosa 1.4 1.2 4.1 7.7 6 Versicolour 2.4 6.8 3.6 5.5 5 Verginica 1.1 2.5 3.4 3 4 Setosa 2.2 2.4 3.9 7 2 Versicolour 2.3 6.3 3 6.8 1 Class P-width P-length S-width S-length

19. Data Verginica 2.6 3 2 3 Versicolour 5.6 4.2 6 9 Verginica 2.8 3.7 1 8 Setosa 1.6 4.3 6.3 7 Setosa 1.2 4.1 7.7 6 Versicolour 6.8 3.6 5.5 5 Verginica 2.5 3.4 3 4 Setosa 2.4 3.9 7 2 Versicolour 6.3 3 6.8 1 Class P-length S-width S-length

21. Construct example decision tree

22. Tree and Rules Learned S-length <5.5  5.5 3,4,8 P-length  5.6 ≤ 2.4 1,5,9 2,6,7 If S-length < 5.5., then Verginica If S-length  5.5 and P-length  5.6, then Versicolour If S-length  5.5 and P-length ≤ 2.4, then Setosa

27. Example finance 20 6 5 7 other 28 15 0 10 finance 25 11 0 9 other 35 2 0 8 finance 25 4 9 6 finance 20 2 8 5 other 14 7 5 4 other 25 7 7 3 finance 35 8 6 2 other 40 3 0 1 class the rolling stock

31. Information based on attributes = Remainder (A) P=n=10, so H(1/2,1/2)= 1 bit

33. Example finance 20 6 5 7 other 28 15 0 10 finance 25 11 0 9 other 35 2 0 8 finance 25 4 9 6 finance 20 2 8 5 other 14 7 5 4 other 25 7 7 3 finance 35 8 6 2 other 40 3 0 1 class the rolling stock

34. stock rolling <5 5-10  10  10 5-10 <5 1,8,9,10 2 , 3,4,5,6,7 1 , 5,6,8 2,3,4,7 9,10 Gain(stock)=1-[4/10H(1/10,3/10)+6/10H(4/10,2/10)]= 1-[.4((-.1* -3.32)-(.3*-1.74))+.6((-.4*-1.32)-(.2*-2.32))]= 1-[.303+.5952]=.105 Gain(rolling)=1-[4/10H(1/2,1/2)+4/10H(1/2,1/2)+2/10H(1/2,1/2)]=0