We will use the example from the Chapter 2, Naive Bayes and Chapter 3, Decision Tree, again.
|
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.
Analysis:
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 be m=min(M,math.ceil(2*math.sqrt(M)))=min(M,math.ceil(2*math.sqrt(4)))=4.
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']]
When constructing a random decision tree as a part of a random forest, we will choose only a subset of them in a random way with replacement.