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Naive Bayes Algorithm
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A machine learning method called Naive Bayes is based on the probability principle.
The Bayes Theorem. is used to explain how dependent events are related to one another Conditional Probability is the likelihood of occurrence A provided that event B has already occurred.
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| Y (Whether the email is spam or not) | X (Whether the email contains the word lottery or not) |
|---|---|
| Spam | Lottery |
| Not Spam | Lottery |
| Spam | No Lottery |
| Spam | No Lottery |
| Not Spam | No Lottery |
| Not Spam | No Lottery |
| Not Spam | Lottery |
| Not Spam | Lottery |
| Spam | No Lottery |
| Spam | No Lottery |
P(Data) = P(Lottery) = No. of times lottery appears in the data / Total no. of emails = 4/10 P(Class) = P(Spam) = No. of times spam appears in the data P(Data) = P(Lottery) = No. of times lottery appears in the data. Considering that emails are spam, (Data | Class) = P(Lottery | Spam) = No. of emails containing the phrase lottery = 1/5. There are five spam emails in all, one of which contains the term lottery.
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