Data Science Algorithms in a Week by David Natingga

Author:David Natingga [Natingga, David]
Language: eng
Format: azw3, pdf
Publisher: Packt Publishing
Published: 2017-08-16T04:00:00+00:00

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']]

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.

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