```
tags:
- AI/Algorithms/BayesModel
- AI/ML/SupervisedLearning
- AI/Tasks/Classification
- AI/Tasks/Prediction
- Mathematics/Statistics/Bayes
aliases:
- Naive Bayes Classification
```

Naive Bayes is a Classification and prediction technique that is based on the Bayes Theorem. It predicts that the probabilities for each class belongs to a particular class and that the class with the highest probability is considered the most likely class.

Notes:

- It's called Naive because it assumes the independence between attributes of data points(the occurrence of a certain feature is independent of the occurrence of other features).

Applications:

Types:

Advantages:

- It's simple and easy to implement.
- It works well with high-dimensional datasets.
- It can handle both continuous and discrete data.
- It's good at handling missing data.
- Performs both binary and multi-class classification.
- It performs well even with a small amount of training data.

Disadvantages:

- The algorithm is sensitive to the quality of the input data.
- Cannot handle negative correlation between input features.
- The assumption of independence among input features may not always hold true.
- Requires a large amount of memory to store the Conditional Probability for each input feature.

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