Advantage and disadvantage of naive bayes classifier
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In machine learning, naive Bayes classifiersare a family of simple "probabilistic classifiers" based on applying Bayes' theoremwith strong (naive) independenceassumptions between the features.
Naive Bayes has been studied extensively since the 1950s. It was introduced under a different name into the text retrievalcommunity in the early 1960s,[1]:488 and remains a popular (baseline) method for text categorization, the problem of judging documents as belonging to one category or the other (such as spam or legitimate, sports or politics, etc.) with word frequencies as the features. With appropriate pre-processing, it is competitive in this domain with more advanced methods including support vector machines.[2] It also finds application in automatic medical diagnosis.[3]
Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem. Maximum-likelihoodtraining can be done by evaluating a closed-form expression,[1]:718 which takes linear time, rather than by expensive iterative approximation as used for many other types of classifiers.
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Naive Bayes has been studied extensively since the 1950s. It was introduced under a different name into the text retrievalcommunity in the early 1960s,[1]:488 and remains a popular (baseline) method for text categorization, the problem of judging documents as belonging to one category or the other (such as spam or legitimate, sports or politics, etc.) with word frequencies as the features. With appropriate pre-processing, it is competitive in this domain with more advanced methods including support vector machines.[2] It also finds application in automatic medical diagnosis.[3]
Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem. Maximum-likelihoodtraining can be done by evaluating a closed-form expression,[1]:718 which takes linear time, rather than by expensive iterative approximation as used for many other types of classifiers.
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The advantages of Naive Bayes classifier are as follows:
1. They are very easy for implementing.
2. For estimating the parameters they only needs a very few amount of training data.
3. In many cases, the results are goods.
The disadvantages of Naive Bayes classifier are as follows:
1. Chance of loss of accuracy.
2. Naive Bayes classifier cannot modify dependencies because dependencies exist between variables.
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