[amp_mcq option1=”Attributes are equally important.” option2=”Attributes are statistically dependent of one another given the class value.” option3=”Attributes are statistically independent of one another given the class value.” option4=”Attributes can be nominal or numeric” correct=”option2″]
The correct answer is: B. Attributes are statistically dependent of one another given the class value.
Naive Bayes is a probabilistic machine learning algorithm that assumes the attributes are statistically independent of one another given the class value. This means that the probability of an attribute taking a certain value is not affected by the values of the other attributes. This assumption is often violated in real-world data, which can lead to poor performance of Naive Bayes.
Option A is incorrect because attributes are not always equally important. Some attributes may be more important than others in determining the class value.
Option C is correct because Naive Bayes assumes that the attributes are statistically independent of one another given the class value.
Option D is correct because attributes can be nominal or numeric in Naive Bayes.