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How does multinomial naive Bayes work?
What is the Multinomial Naive Bayes algorithm? The algorithm is based on the Bayes theorem and predicts the tag of a text such as a piece of email or newspaper article. It calculates the probability of each tag for a given sample and then gives the tag with the highest probability as output.
Why do we use multinomial Naive Bayes?
The term Multinomial Naive Bayes simply lets us know that each p(fi|c) is a multinomial distribution, rather than some other distribution. This works well for data which can easily be turned into counts, such as word counts in text.
How to calculate posterior probability in naive Bayes classifier?
Naive Bayes classifier calculates the probability of an event in the following steps: Step 1: Calculate the prior probability for given class labels Step 2: Find Likelihood probability with each attribute for each class Step 3: Put these value in Bayes Formula and calculate posterior probability.
What’s the difference between a naive Bayes and a multinomial?
The general term Naive Bayes refers the the strong independence assumptions in the model, rather than the particular distribution of each feature. A Naive Bayes model assumes that each of the features it uses are conditionally independent of one another given some class.
What is p ( x ) in a naive Bayesian model?
P(x)is the prior probability of predictor. In ZeroR model there is no predictor, in OneR model we try to find the single best predictor, naive Bayesian includes all predictors using Bayes’ rule and the independence assumptions between predictors. Example 1: We use the same simple Weather dataset here.
How is the posterior probability of a target calculated?
The posterior probability can be calculated by first, constructing a frequency table for each attribute against the target. Then, transforming the frequency tables to likelihood tables and finally use the Naive Bayesian equation to calculate the posterior probability for each class.