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Questions and Answers
Naive Bayes classifiers are based on applying which theorem?
Naive Bayes classifiers are based on applying which theorem?
- Law of Large Numbers
- Central Limit Theorem
- Bayes' theorem (correct)
- Poisson's theorem
Naive Bayes classifiers assume what type of independence between the features?
Naive Bayes classifiers assume what type of independence between the features?
- Weak dependence
- No dependence (correct)
- Strong dependence
- Moderate dependence
What is one advantage of Naive Bayes classifiers?
What is one advantage of Naive Bayes classifiers?
- They are highly scalable (correct)
- They achieve low accuracy levels
- They require a large number of parameters
- They require expensive iterative approximation
What is another name for Naive Bayes models in the statistics literature?
What is another name for Naive Bayes models in the statistics literature?
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How is maximum-likelihood training done for Naive Bayes classifiers?
How is maximum-likelihood training done for Naive Bayes classifiers?
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Study Notes
Naive Bayes Classifiers
- Naive Bayes classifiers are based on applying Bayes' theorem.
- Naive Bayes classifiers assume independence between the features, meaning that the presence or absence of a particular feature does not affect the presence or absence of any other feature.
- One advantage of Naive Bayes classifiers is that they are easy to implement and computationally efficient.
- Naive Bayes models are also known as simple Bayes or independence Bayes in the statistics literature.
- Maximum-likelihood training for Naive Bayes classifiers is done by finding the parameters that maximize the likelihood of the training data.
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