Naïve Bayes
Explore the Naive Bayes algorithm to understand how probabilities and Bayes' theorem help classify data points. Learn about prior, likelihood, and posterior probabilities along with different Naive Bayes variants like Gaussian, Bernoulli, and Multinomial to prepare for machine learning interviews.
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This Naive Bayes technique is based on probabilities. The probability of an event happening or not happening can be calculated using past data.
Consider the problem of predicting the temperatures for next week using the current temperature. This seems very difficult. However, if you have the last five or ten years of temperature data, you can do this more efficiently and with higher accuracy. The Naïve Bayes algorithm is based on this concept.
Here we will provide some examples for probability and move forward toward building the algorithm.
Bayes' Law
According to the Bayes law, for two events A and B, if we know the conditional probability of event B given A and the probabilities of event A and B, then we can calculate the probability of B given A. We can represent it as:
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