We will again use the examples from Chapter 2, Naive Bayes, and Chapter 3, Decision Tree, as follows:
Temperature | Wind | Sunshine | Play |
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 | None | Sunny | Yes |
Hot | Strong | Cloudy | Yes |
Warm | None | Cloudy | Yes |
Warm | Strong | Sunny | ? |
However, we would like to use a random forest consisting of four random decision trees to find the result of the classification.
We are given M=4 variables from which a feature can be classified. Thus, we choose the maximum number of the variables considered at the node to:
We are given the following features:
[['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', 'None', 'Sunny', 'Yes'], ['Hot', 'Strong', 'Cloudy', 'Yes'], ['Warm', 'None', 'Cloudy', 'Yes...