# What is Naive Bayes?

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# What is Naive Bayes?

## An introduction to machine learning algorithms

Naive Bayes algorithm is a supervised learning algorithm(probabilistic machine learning algorithm), which is based on Bayes theorem, used in a wide variety of classification tasks in machine learning.

In this article, I would be giving you a detailed explanation and how this model works.

# What is Naive Bayes?

Naive Bayes is primarily used in text classification with a large training dataset. The Naive Bayes Classifier is a simple and effective classification algorithm that aids in the development of fast machine learning models that can make accurate predictions. It’s a probabilistic classifier, which means it makes predictions based on the likelihood of an object. The Nave Bayes Algorithm is commonly used for spam filtration, sentiment analysis, and article classification.

# What is Bayes Theorem?

The Bayes’ Theorem is a simple formula that can be used to calculate conditional probabilities.
conditional probability — A measure of the possibility of an event occurring if another event has already occurred is called conditional probability (by assumption, presumption, assertion, or evidence).

Posterior probability (P(A|B)): is the probability of hypothesis A on the observed event B.

Likelihood probability P(B|A): stands for Likelihood, which is the probability of the evidence provided that a hypothesis’ probability is true.

Prior Probability (P(A)): is the probability of a hypothesis before seeing the evidence.
Marginal Probability P(B): stands for Probability of Evidence Marginal Probability.

Which tells us how likely A is if B occurs, written P(A|B), also known as posterior probability. When we know how often B occurs when A occurs, written P(B|A), and how likely A is on its own, written P(A), and how likely B is on its own, written P(B), we can write P(B|A) (B).

# Working of Naive Bayes’ Classifier:

Working of Naive Bayes’ Classifier can be understood with the help of the below example:

Assume we have a dataset of weather conditions and a target variable called “Play.” So, utilising this dataset, we must select whether or not to play on a specific day based on the weather circumstances. To fix this problem, we must take the following steps:

Convert the given dataset into frequency tables.

1. Find the probability of given features to generate a Likelihood table.
2. Calculate the posterior probability using Bayes’ theorem.

# Types of Naive Bayes Model:

There are three types of Naive Bayes Model, which are given below:

• Gaussian: The Gaussian model assumes that traits are regularly distributed. The model implies that continuous values are drawn from a Gaussian distribution if predictors take continuous values rather than discrete values.
• Multinomial: When the data is multinomial distributed, the Multinomial Nave Bayes classifier is utilised. It’s mostly used to tackle document classification problems, such as determining which category a document belongs to, such as Sports, Politics, or Education.
• Bernoulli: The Bernoulli classifier is identical to the Multinomial classifier, with the exception that the predictor variables are independent Booleans variables. Determine whether a given word appears in a document, for example. For jobs involving document classification, this paradigm is well-known.

# Advantages of Naive Bayes Classifier:

• Nave Bayes is a fast and simple machine learning technique for predicting a class of datasets.
• It’s suitable for both binary and multi-class classifications.
• In comparison to the other Algorithms, it performs better in Multi-class predictions.
• It is the most popular choice for text classification problems.

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# Conclusion

so I hope today you guys have a good understanding in the near future Naive Bayes I would be making more articles in which I will be explaining moremodels and would be making an article to make implement Naive Bayes with source code.

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