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Assume you are given data consisting of 14 training examples (see below). Using the training data, construct a decision tree for the binary classification of
Assume you are given data consisting of 14 training examples (see below). Using the training data, construct a decision tree for the binary classification of the player into playing tennis or not playing tennis. To do this, use the information gain (IG) to select which attribute to split on. Show each of your steps and computation!
Day | Sunny | Temp. | Humidity | Wind | PlayTennis |
D1 | Sunny | Hot | High | Weak | No |
D2 | Sunny | Hot | High | Strong | No |
D3 | Overcast | Hot | High | Weak | Yes |
D4 | Rain | Mild | High | Weak | Yes |
D5 | Rain | Cool | Normal | Weak | Yes |
D6 | Rain | Cool | Normal | Strong | No |
D7 | Overcast | Cool | Normal | Strong | Yes |
D8 | Sunny | Mild | High | Weak | No |
D9 | Sunny | Cool | Normal | Weak | Yes |
D10 | Rain | Mild | Normal | Weak | Yes |
D11 | Sunny | Mild | Normal | Strong | Yes |
D12 | Overcast | Mild | High | Strong | Yes |
D13 | Overcast | Hot | Normal | Weak | Yes |
D14 | Rain | Mild | High | Strong | No |
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