Naïve Bayes(Machine Learning)

2 min readJun 29, 2021

Naïve Bayes is a classification technique based on Bayes Theorem & is one of the fastest Machine Learning algorithm to predict a class of datasets. Binary as well as Multi-class classifications can be done using it. It is easy to build.

What it does?

It assumes that the presence of particular feature in a class is unrelated to the presence of any other feature.

It works on Bayes Theorem.

Types

Gaussian: It assumes that features follow a normal distribution. This means if predictors take continuous values instead of discrete, then the model assumes that these values are sampled from the Gaussian distribution.
Multinomial: It is used when the data is multinomial distributed. It is primarily used for document classification problems, it means a particular document belongs to which category such as Sports, Politics, education, etc.
The classifier uses the frequency of words for the predictors.
Bernoulli: It works similar to the Multinomial classifier, but the predictor variables are the independent Booleans variables. Such as if a particular word is present or not in a document. This model is also famous for document classification tasks.

(Reference- javatpoint.com)

Applications

Text Classification.
Medical Data Classification.
Sentiment Analysis.
SPAM Filtering.
Object & Face Recognition.

and many more…

Using Naïve Bayes

Way 1- Directly importing from scikit learn library in Python.

Input- from sklearn.naive_bayes import GaussianNB

This imports the Gaussian model.

Input- from sklearn.naive_bayes import BernoulliNB

This imports the Bernoulli model.

Input- from sklearn.naive_bayes import MultinomialNB

This imports the Multinomial model.

Way 2- Implementing Naïve Bayes from Scratch.

This was all about Naïve Bayes Classifier.

Happy Learning Folks!

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