The document discusses the Naive Bayes classifier and how it works using conditional probability. It provides examples of where Naive Bayes can be used, such as face recognition, weather prediction, and medical diagnosis. The document then gives a demonstration of how Naive Bayes classification would predict if a fruit is a banana, orange, or other based on features like length, sweetness, and color.

1. Naive Bayes
Md. Anik Hasan
Id: 201-35-572
Sec: A
Department Of SWE
At Daffodil International University

2. The Naive Bayes classifier
works on the principle of
conditional probability
What is
Naive
classifier?

3. Where is Naive Bayes Used?
You can use Naive Bayes for the following things:
• Face Recognition
As a classifier, it is used to identify the faces or its other features, like
nose, mouth, eyes, etc.
• Weather Prediction
It can be used to predict if the weather will be good or bad.
• Medical Diagnosis.
the Naive Bayes Classifier gives the conditional probability of an event
A given event B.

4. Let us use the following demo to understand the concept of a Naive Bayes classifier:

5. Say you have 1000
fruits which could be
either ‘banana’,
‘orange’ or ‘other’.
These are the 3
possible classes of the
Y variable.

6. So the objective of the classifier is to predict
if a given fruit is a ‘Banana’ or ‘Orange’ or
‘Other’ when only the 3 features (long, sweet
and yellow) are known.

7. Let’s say you are given a fruit that is: Long, Sweet and Yellow, can you predict what
fruit it is?
So the respective priors are 0.5, 0.3 and 0.2. P(Y=Banana) = 500 / 1000 = 0.50
P(Y=Orange) = 300 / 1000 = 0.30 P(Y=Other) = 200 / 1000 = 0.20