Page 99 - Data Science Algorithms in a Week

Random Forest

Playing chess example

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']]

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