machine_learning_exercise3 - Change Weak Rainy Sameecast...

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Sky AirTemp Rainy Cold Warm Sunny Humidity Wind Weak Strong Normal High Water Forecast Warm Cool Change Same Example Sky AirTemp Humidity Wind Water Forecast EnjoySport 1 Sunny Warm Normal Strong Warm Same Yes 2 Sunny Warm High Strong Warm Same Yes 3 Rainy Cold High Strong Warm Change No 4 Sunny Warm High Strong Cool Change Yes TABLE 2.1 Positive and negative training examples for the target concept EnjoySport. 3.4. ID3 searches for just one consistent hypothesis, whereas the CANDIDATE-ELIMINATION algorithm finds all consistent hypotheses. Consider the correspondence between these two learning algorithms. (a) Show the decision tree that would be learned by ID3 assuming it is given the four training examples for the Enjoy Sport? target concept shown in Table 2.1 of Chapter 2. E=-(3/4)*log2(3/4)-(1/4)*log2(1/4)=0.811 S=[3+,1-], E=0.811 S=[3+,1-], E=0.811 S=[3+,0-] S=[0+,1-] S=[3+,0-] S=[0+,1-] E=0 E=0 E=0 E=0 S=[3+,1-], E=0.811 S=[3+,1-], E=0.811 S=[0+,0-] S=[3+,1-] S=[2+,1-] S=[1+,0-] E=0 E=0.811 E=0.918 E=0 E(Strong) = -(3/4)*log2(3/4)-(1/4)*log2(1/4) = 0.811 E(High) = -(2/3)*log2(2/3)-(1/3)*log2(1/3) = 0.918 S=[3+,1-], E=0.811 S=[3+,1-], E=0.811
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