What does the term "naive" refer to in Naive Bayes classification? a. It indicates that the model relies on basic statistical principles. b. It reflects the assumption of independence among features. c. It implies that the model performs well under various conditions. d. It suggests that the model is simple and easy to implement.

What is the Naive Bayes classifier used for?Select one:a.To classify data into different categories based on certain featuresb.To predict the value of a continuous variablec.All of the aboved.To predict the probability of an event occurring

he Naïve Bayes classifier assumes that (Select ANY correct answer)A.the attributes individually follow a Gaussian conditional probability distribution, given the class.B.the attributes individually follow a Gaussian probability distribution, independent of the class.C.the value of any attribute is statistically independent of the value of any other attribute (given the class value).D.the value of any attribute is statistically dependent of the value of any other attribute (given the class value).

Which of the following statements about Naive Bayes classification is true?It assumes that all features are independent of each other.It works well only with numerical data.It is a type of unsupervised learning algorithm.It is primarily used for regression tasks.

What is the assumption made by the Naive Bayes classifier?Select one:a.That the features in the data are dependent on each otherb.That the features in the data are independent of each otherc.That the features in the data are normally distributedd.That the features in the data are uniformly distributed

What is the main assumption of the Naive Bayes classifier?Answer areaThe attributes are highly dependentThe attributes are equally importantThe attributes are conditionally independent given the classThe attributes are unimportant for classification