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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.

- Find the
**probability**of given features to generate a Likelihood table. - 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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