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About Lecture Notes - Naive Bayes New
- The document discusses Naive Bayes, a probabilistic classification algorithm. It explains key concepts like probability, conditional probability, joint probability, and Bayes' theorem. - Naive Bayes makes a strong independence assumption that features are conditionally independent given the class. This allows it to estimate probabilities by counting occurrences. - The document provides examples of how Naive Bayes classifies text documents using a bag-of-words representation and calculating posterior probabilities to assign classes. Laplace smoothing helps address zero probabilities.
Detailed Information
Author: | ['Amandeep Kaur Gahir'] |
---|---|
Publication Year: | 2021 |
Pages: | 8 |
Language: | English |
Format: | |
Price: | FREE |
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