Understanding Naive Bayes Classifier

What is Naive Bayes?

Let's start with a basic introduction to the Bayes theorem, named after Thomas Bayes from the 1700s. The Naive Bayes classifier works on the principle of conditional probability, as given by the Bayes theorem.

Let us go through some of the simple concepts of probability that we will use. Consider the following example of tossing two coins. If we toss two coins and look at all the different possibilities, we have the sample space as:{HH, HT, TH, TT}

While calculating the math on probability, we usually denote probability as P. Some of the probabilities in this event would be as follows:

• The probability of getting two heads = 1/4 
• The probability of at least one tail = 3/4 
• The probability of the second coin being head given the first coin is tail = 1/2 
• The probability of getting two heads given the first coin is a head = 1/2

The Bayes theorem gives us the conditional probability of event A, given that event B has occurred. In this case, the first coin toss will be B and the second coin toss A. This could be confusing because we've reversed the order of them and go from B to A instead of A to B.

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According to Bayes theorem:

Let us apply Bayes theorem to our coin example. Here, we have two coins, and the first two probabilities of getting two heads and at least one tail are computed directly from the sample space.

Now in this sample space, let A be the event that the second coin is head, and B be the event that the first coin is tails. Again, we reversed it because we want to know what the second event is going to be. 

We're going to focus on A, and we write that out as a probability of A given B:

Probability = P(A|B)

= [ P(B|A) * P(A) ] / P(B)

= [ P(First coin being tail given the second coin is the head) * P(Second coin being

head) ] / P(First coin being tail)

= [ (1/2) * (1/2) ] / (1/2)

= 1/2 = 0.5

Bayes theorem calculates the conditional probability of the occurrence of an event based on prior knowledge of conditions that might be related to the event. 

Like with any of our other machine learning tools, it's important to understand where the Naive Bayes fits in the hierarchy. 

Understanding Naive Bayes and Machine Learning 

Machine learning falls into two categories:

• Supervised learning 
• Unsupervised learning

Supervised learning falls into two categories:

• Classification 
• Regression

Naive Bayes algorithm falls under classification.

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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 

Doctors can diagnose patients by using the information that the classifier provides. Healthcare professionals can use Naive Bayes to indicate if a patient is at high risk for certain diseases and conditions, such as heart disease, cancer, and other ailments. 

News Classification 

With the help of a Naive Bayes classifier, Google News recognizes whether the news is political, world news, and so on. 

As the Naive Bayes Classifier has so many applications, it’s worth learning more about how it works.

Advantages of Naive Bayes Classifier 

The following are some of the benefits of the Naive Bayes classifier: 

• It is simple and easy to implement
• It doesn’t require as much training data
• It handles both continuous and discrete data
• It is highly scalable with the number of predictors and data points
• It is fast and can be used to make real-time predictions
• It is not sensitive to irrelevant features 

How much do you know about the algorithm called Naive Bayes? Try answering these Caltech AI Course and find out now. 

Use Case - Text Classification 

Text classification is one of the most popular applications of a Naive Bayes classifier.

Problem statement: To perform text classification of news headlines and classify news into different topics for a news website.

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