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What fundamental principle does the Naive Bayes Classifier utilize for classification?
Why is the Naive Bayes Classifier considered ‘naive’?
In the context of Naive Bayes, what does P(A|B) represent?
Which of the following is a real-world application of the Naive Bayes Classifier?
What is a key difference between Naive Bayes and Logistic Regression?
What challenge does the Naive Bayes Classifier face when dealing with continuous variables?
What does the ‘independence assumption’ in Naive Bayes refer to?
Which metric would you look at to evaluate the performance of a Naive Bayes Classifier?
What is a limitation of the Naive Bayes Classifier?
Why is Naive Bayes considered to be efficient with large datasets?
What does Bayes’ theorem allow us to update in the context of Naive Bayes Classifier?
Which of the following scenarios best represents a real-world application of Naive Bayes?
The ‘naive’ assumption of the Naive Bayes Classifier is that features are:
In Naive Bayes, the term P(Class|Features) is known as:
What type of Naive Bayes model is best suited for dealing with continuous data?
Which of the following best describes the role of the feature independence assumption in Naive Bayes?
When comparing Naive Bayes to Logistic Regression, Naive Bayes:
Which is a common method to handle the zero frequency problem in Naive Bayes?
What does the term ‘Gaussian’ in Gaussian Naive Bayes refer to?
In the Iris Dataset example, what is the goal of applying Naive Bayes?
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