Naive Bayes Classification Calculations (Part 1)

In this post, I will discuss classification calculations using the Naive Bayes method.

What we will classify is the Roadworthy BUS Classification

The data to be used consists of the following:

• Number of data = 14
• Noise = 4
• Number of classifications = 2 

From the data above, what we have to do is:

1. Determine class probabilities

• Roadworthy = 9/15 = 0.6
• Not Roadworthy = 6/15 = 0.4

Information :

• 15 is the amount of data
• 9 is the amount of data for Roadworthy
• 6 is the amount of data for Not Roadworthy

2. Determine category probabilities

• Roadworthy Category

Information :

• P(Driver=Healthy|Road Worthy) is the number of Healthy and Roadworthy Driver categories
• 5 is the number of healthy drivers who are roadworthy
• 9 is the number of roadworthy classifications
• So the result is 0.555556
• Likewise with the next process

• Not Roadworthy Category

Information :

• P(Driver=Healthy|Unfit for Road) is the number of categories of Healthy and Unfit for Road drivers
• 2 is the number of healthy drivers who are not roadworthy
• 6 is the number of classifications not roadworthy
• So the result is 0.333333
• Likewise with the next process

After the probability calculation process is carried out, the data that will be obtained is as follows :