8. CAN WE PLAY TENNIS
Outlook Temperature Humidity Wind PlayTennis
Sunny Hot High Weak No
Sunny Hot High Strong No
Overcast Hot High Weak Yes
Rain Mild High Weak Yes
Rain Cool Normal Weak Yes
Rain Cool Normal Strong No
Overcast Cool Normal Strong Yes
Sunny Mild High Weak No
Sunny Cool Normal Weak Yes
Rain Mild Normal Weak Yes
Sunny Mild Normal Strong Yes
Overcast Mild High Strong Yes
Overcast Hot Normal Weak Yes
Rain Mild High Strong No Dataset from Mitchell, T. M. Machine
Learning. McGraw-Hill, 1997. pp. 59-
60.
9. CAN WE PLAY TENNIS
Outlook Temperature Humidity Wind PlayTennis
Sunny Hot High Weak No
Sunny Hot High Strong No
Overcast Hot High Weak Yes
Rain Mild High Weak Yes
Rain Cool Normal Weak Yes
Rain Cool Normal Strong No
Overcast Cool Normal Strong Yes
Sunny Mild High Weak No
Sunny Cool Normal Weak Yes
Rain Mild Normal Weak Yes
Sunny Mild Normal Strong Yes
Overcast Mild High Strong Yes
Overcast Hot Normal Weak Yes
Rain Mild High Strong No
10. CAN WE PLAY TENNIS
Outlook PlayTennis
Sunny No
Sunny No
Overcast Yes
Rain Yes
Rain Yes
Rain No
Overcast Yes
Sunny No
Sunny Yes
Rain Yes
Sunny Yes
Overcast Yes
Overcast Yes
Rain No
11. CAN WE PLAY TENNIS
Outlook PlayTennis
Overcast Yes
Overcast Yes
Overcast Yes
Overcast Yes
Rain Yes
Rain Yes
Rain Yes
Rain No
Rain No
Sunny Yes
Sunny Yes
Sunny No
Sunny No
Sunny No
12. CAN WE PLAY TENNIS
Outlook PlayTennis
Overcast Yes
Overcast Yes
Overcast Yes
Overcast Yes
Rain Yes
Rain Yes
Rain Yes
Rain No
Rain No
Sunny Yes
Sunny Yes
Sunny No
Sunny No
Sunny No
13. CAN WE PLAY TENNIS
Outlook PlayTennis
Overcast Yes
Overcast Yes
Overcast Yes
Overcast Yes
Rain Yes
Rain Yes
Rain Yes
Rain No
Rain No
Sunny Yes
Sunny Yes
Sunny No
Sunny No
Sunny No
𝑒𝑛𝑡𝑟𝑜𝑝𝑦 𝐻(𝑥) = −𝑝 𝑦𝑒𝑠 log2(𝑝 𝑦𝑒𝑠) −𝑝 𝑛𝑜 log2(𝑝 𝑛𝑜)
14. CAN WE PLAY TENNIS
Outlook PlayTennis
Overcast Yes
Overcast Yes
Overcast Yes
Overcast Yes
Rain Yes
Rain Yes
Rain Yes
Rain No
Rain No
Sunny Yes
Sunny Yes
Sunny No
Sunny No
Sunny No
𝑒𝑛𝑡𝑟𝑜𝑝𝑦 𝑂𝑣𝑒𝑟𝑐𝑎𝑠𝑡 = −1 log2 1 − 0 log2 0 = 0
𝑒𝑛𝑡𝑟𝑜𝑝𝑦 𝑅𝑎𝑖𝑛 = −0.6 log2 0.6 − 0.4 log2 0.4 = 0.97
𝑒𝑛𝑡𝑟𝑜𝑝𝑦𝑆𝑢𝑛𝑛𝑦 = −0.4 log2 0.4 − 0.6 log2 0.6 = 0.97
15. CAN WE PLAY TENNIS
Outlook PlayTennis
Overcast Yes
Overcast Yes
Overcast Yes
Overcast Yes
Rain Yes
Rain Yes
Rain Yes
Rain No
Rain No
Sunny Yes
Sunny Yes
Sunny No
Sunny No
Sunny No
𝑒𝑛𝑡𝑟𝑜𝑝𝑦 𝑂𝑣𝑒𝑟𝑐𝑎𝑠𝑡 = −1 log2 1 − 0 log2 0 = 0
𝑒𝑛𝑡𝑟𝑜𝑝𝑦 𝑅𝑎𝑖𝑛 = −0.6 log2 0.6 − 0.4 log2 0.4 = 0.97
𝑒𝑛𝑡𝑟𝑜𝑝𝑦𝑆𝑢𝑛𝑛𝑦 = −0.4 log2 0.4 − 0.6 log2 0.6 = 0.97
16. CAN WE PLAY TENNIS
Outlook PlayTennis
Overcast Yes
Overcast Yes
Overcast Yes
Overcast Yes
Rain Yes
Rain Yes
Rain Yes
Rain No
Rain No
Sunny Yes
Sunny Yes
Sunny No
Sunny No
Sunny No
𝑒𝑛𝑡𝑟𝑜𝑝𝑦 𝐵𝑒𝑓𝑜𝑟𝑒 = 0.94 𝑒𝑛𝑡𝑟𝑜𝑝𝑦 𝑎𝑓𝑡𝑒𝑟 = 0.7
𝑖𝑛𝑓𝑜𝑟𝑚𝑎𝑡𝑖𝑜𝑛 𝑔𝑎𝑖𝑛 = 0.94 − 0.7 = 0.24
17. CAN WE PLAY TENNIS
Outlook Temperature Humidity Wind PlayTennis
Sunny Cool Normal Weak Yes
Sunny Mild Normal Strong Yes
Sunny Hot High Weak No
Sunny Hot High Strong No
𝑂𝑢𝑡𝑙𝑜𝑜𝑘?
𝑅𝑎𝑖𝑛
𝑌𝑒𝑠
Outlook Temperature Humidity Wind PlayTennis
Rain Mild High Weak Yes
Rain Cool Normal Weak Yes
Rain Mild Normal Weak Yes
Rain Cool Normal Strong No
18. CAN WE PLAY TENNIS
Outlook Temperature Humidity Wind PlayTennis
Sunny Cool Normal Weak Yes
Sunny Mild Normal Strong Yes
Sunny Hot High Weak No
Sunny Hot High Strong No
𝑂𝑢𝑡𝑙𝑜𝑜𝑘?
𝑅𝑎𝑖𝑛
𝑌𝑒𝑠
𝑊𝑖𝑛𝑑?
𝑌𝑒𝑠
𝑁𝑜
19. CAN WE PLAY TENNIS
𝑂𝑢𝑡𝑙𝑜𝑜𝑘?
𝑅𝑎𝑖𝑛
𝑌𝑒𝑠
𝑊𝑖𝑛𝑑?
𝑌𝑒𝑠
𝑁𝑜
𝑌𝑒𝑠
𝑁𝑜
𝐻𝑢𝑚𝑖𝑑𝑖𝑡𝑦?
20. CREATING TREE
DecisionVariable[] attributes =
{
new DecisionVariable("Outlook", 3),
new DecisionVariable("Temperature", 3),
new DecisionVariable("Humidity", 2),
new DecisionVariable("Wind", 2)
};
int classCount = 2;
DecisionTree tree = new DecisionTree(attributes, classCount);