Introduction to Naive Bayes Algorithm The Naive Bayes algorithm is a simple, probabilistic machine learning method used for classification tasks. It’s based on Bayes’ Theorem, which calculates the probability of an event given prior knowledge. The “naive” part comes from its assumption that features (input variables) are independent of each other, which simplifies computations but may not always hold true in real-world data.
Main Idea Behind Naive Bayes Algorithm
The main idea behind Naive Bayes Algorithm is Bayes Theorem which is based on conditional probability and Bayes theorem.
Conditional Probability and Bayes Theorem
Conditional probability, important concept in probabilistic modeling, allows us to update probabilistic models when additional information is revealed.
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Introduction to Naive Bayes Algorithm
The Naive Bayes algorithm is a simple, probabilistic machine learning method used for classification tasks. It’s based on Bayes’ Theorem, which calculates the probability of an event given prior knowledge. The “naive” part comes from its assumption that features (input variables) are independent of each other, which simplifies computations but may not always hold true in real-world data.
Main Idea Behind Naive Bayes Algorithm
The main idea behind Naive Bayes Algorithm is Bayes Theorem which is based on conditional probability and Bayes theorem.
Conditional Probability and Bayes Theorem
Conditional probability, important concept in probabilistic modeling, allows us to update probabilistic models when additional information is revealed.
Probability of even A given the event B:
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