5 Questions
Naive Bayes classifiers are based on applying which theorem?
Bayes' theorem
Naive Bayes classifiers assume what type of independence between the features?
No dependence
What is one advantage of Naive Bayes classifiers?
They are highly scalable
What is another name for Naive Bayes models in the statistics literature?
Simple Bayes
How is maximum-likelihood training done for Naive Bayes classifiers?
By evaluating a closed-form expression
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.
Take this quiz to test your knowledge on Naive Bayes classifiers, a family of simple probabilistic classifiers used in statistics. Learn about the strong independence assumptions and their application in achieving high accuracy levels with kernel density estimation. Find out how scalable and efficient Naive Bayes classifiers are in various scenarios.
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