Participate in this quiz to evaluate your knowledge of Naive Bayes, a widely-used classification algorithm in the field of Machine Learning.

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Participate in this quiz to evaluate your knowledge of Naive Bayes, a widely-used classification algorithm in the field of Machine Learning.

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- Question 1 of 10
##### 1. Question

**What does ‘posterior probability’ refer to?**CorrectIncorrect - Question 2 of 10
##### 2. Question

**In Naive Bayes, how is class membership probability (posterior probability) computed?**CorrectIncorrect - Question 3 of 10
##### 3. Question

**In Naive Bayes, the likelihood P(X|Y) is difficult to compute. What technique is commonly used to simplify this computation?**CorrectIncorrect - Question 4 of 10
##### 4. Question

**In Gaussian Naive Bayes, how are the mean and variance parameters estimated for each feature?**CorrectIncorrect - Question 5 of 10
##### 5. Question

**In which scenario might Naive Bayes perform poorly compared to other classifiers?**CorrectIncorrect - Question 6 of 10
##### 6. Question

**In the context of Naive Bayes, what is the purpose of the log transformation when calculating probabilities?**CorrectIncorrect - Question 7 of 10
##### 7. Question

**How does the choice of the smoothing parameter impact the performance of the Naive Bayes classifier?**CorrectIncorrect - Question 8 of 10
##### 8. Question

**When is the Gaussian Naive Bayes particularly suitable?**CorrectIncorrect - Question 9 of 10
##### 9. Question

**How does the choice of the probability distribution (e.g., Bernoulli, Multinomial, Gaussian) in Naive Bayes impact its performance?**CorrectIncorrect - Question 10 of 10
##### 10. Question

**What is the role of feature scaling in Naive Bayes?**CorrectIncorrect

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