Let us take an example from the Chapter 2, Naive Bayes again:
Temperature |
Wind |
Sunshine |
Play |
Cold |
Strong |
Cloudy |
No |
Cold |
Strong |
Cloudy |
No |
Warm |
None |
Sunny |
Yes |
Hot |
None |
Sunny |
No |
Hot |
Breeze |
Cloudy |
Yes |
Warm |
Breeze |
Sunny |
Yes |
Cold |
Breeze |
Cloudy |
No |
Cold |
None |
Sunny |
Yes |
Hot |
Strong |
Cloudy |
Yes |
Warm |
None |
Cloudy |
Yes |
Warm |
Strong |
Sunny |
? |
We would like to find out if our friend would like to play chess with us outside in the park. But this time, we would like to use decision trees to find the answer.
Analysis:
We have the initial set S of the data samples as:
S={(Cold,Strong,Cloudy,No),(Warm,Strong,Cloudy,No),(Warm,None,Sunny,Yes), (Hot,None,Sunny,No),(Hot,Breeze,Cloudy,Yes),(Warm,Breeze,Sunny,Yes),(Cold,Breeze,Cloudy,No),(Cold...