Naïve Bayes
Description
Naïve bayes classifier uses probability models to classify class membership of unknowns. In using this classifier, you should use features that are independent of each other or be prepared to assume that they are independent of each other.
Simple Explanation with Example
The Naïve Bayes classifier classifies data based on the following equation:
probability of class membership based on evidence=(likelihood of evidence based on class membership*probability of outcome)/(probability of evidence)
Table 1. Training data and unknown (last row) of disease and no disease based on a particular test.
Class 
Test 

Disease 
Positive 
Disease 
Positive 
Disease 
Negative 
Disease 
Positive 
Disease 
Negative 
No disease 
Negative 
No disease 
Negative 
No disease 
Positive 
No disease 
Negative 
No disease 
Negative 
Unknown 
Negative 
To classify the unknown in Table 1, we look at the probability of disease and no disease for a negative test.
Probability of Disease based on Negative Test=(likelihood of Negative test based on Disease*probability of Disease )/(probability of Negative test)
Probability of Disease based on Negative Test=(2/5*5/10)/(6/10)
Probability of Disease based on Negative Test=1/3
Similarly, we get probability of no disease based on negative test as (4/5*5/10)/(6/10)=2/3
Therefore, the naïve bayes classifier would classify the unknown (Table 1) as no disease.
Additional Information
By default, the naïve bayes classifier on MLGenius assumes normal distribution for features with numerical values. If you know that you don't have a normal distribution, you can either use transformations to your dataset to obtain a normal distribution, or please contact us for assistance.
Pros and Cons
Pros  Cons 


