“This problem requires you to develop a decision tree to make a wise decision whether or not to go out to play tennis in a specific weather condition. Table 6.18 gives a set of training samples taken from an exercise log over two weeks. Specify all suitable paths on the decision tree that lead to a decision.”
Table 6.18
Weather conditions during two weeks of observation”
| Day | Outlook | Humidity | Windy | Play |
| 1 | Sunny | High | Weak | No |
| 2 | Sunny | High | Strong | No |
| 3 | Overcast | High | Weak | Yes |
| 4 | Rain | High | Weak | Yes |
| 5 | Rain | Normal | Weak | Yes |
| 6 | Rain | Normal | Strong | No |
| 7 | Overcast | Normal | Strong | Yes |
| 8 | Sunny | High | Weak | No |
| 9 | Sunny | Normal | Weak | Yes |
| 10 | Rain | Normal | Weak | Yes |
| 11 | Sunny | Normal | Strong | Yes |
| 12 | Overcast | High | Strong | Yes |
| 13 | Overcast | Normal | Weak | Yes |
| 14 | Rain | High | Strong | No |
“This problem requires you to develop a decision tree to make a wise decision whether or...
4. Reconsider the tennis playing training examples, if B Bayesian Belief Network depicting the conditional independence relationship between the attributes and target classification are shown as follows: Day Outlook Temperature Humidity Wind Play Tennis gh Weak No Sunny Ho gh Sunny Ho Strong No gh Weak Yes Overcast Ho Mild gh Weak Yes Rain Normal Weak Yes Rain Coo Normal Rain Coo Strong No Normal Strong Yes Overcast Cool Mild gh Weak No Sunny Normal Weak Yes 9 Sunny Coo...
An example for Playing Tennis in machine learning: Attributes are Outlook, Temperature, Humidity and Wind. Data set and some statistics are already calculated below: Day Outlook Temperature Humidity Wind Mlay Tennis DI Sunny D2 Sunny D3 Ov ercast D4 Rain DS Rain D6 Rain D7 Overast DS Sunny D9 Sunny DIO Rain DI Sunny DI2 Overcast D13 Ovencast D1411 Rain Hot Hot Hot Mild Cool Cool Cool Mild Cool Mild Mild Mild High Weak High Strong No High Weak ligh...
Given the following six instances each with five attributes (Outlook, Temperature, Humidity, Wind, Day) and one class label, calculate Entropy of the whole system • calculate Information gain for attribute "Outlook" • calculate Gini-index for attribute "Outlook" • What is the information gain and Gini-index for attribute “Day Explain why “Day" is NOT a good feature being used as the root node of a decision tree. How to avoid using “Day” as the root node to create the tree ID...
. Question 3 (5 pts]: Given the following six instances cach with five attributes (Outlook, Temperature, Humidity, Wind, Day) and one class label, calculate Entropy of the whole system [1 pt] • calculate Information gain for attribute "Outlook" [1 pt] • calculate Gini-index for attribute "Outlook" [1 pt] What is the information gain and Gini-index for attribute "Day" [pt] • Explain why "Day" is NOT a good feature being used as the root node of a decision tree. How to...