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Question12 Which of the following statements is/are CORRECT about Naïve Bayes? (you can choose more than one)Select one or more alternatives: Naïve Bayes assumes that samples are statistically independent given the class value Naïve Bayes can be applied to both numerical and categorical features If the conditional independence assumption gets violated, Naïve Bayes will be useless Naïve Bayes is a generative model ResetMaximum marks: 1.5 Flag question undefined
Options
A.Naïve Bayes assumes that samples are statistically independent given the class value
B.Naïve Bayes can be applied to both numerical and categorical features
C.If the conditional independence assumption gets violated, Naïve Bayes will be useless
D.Naïve Bayes is a generative model
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Question: Which statements about Naïve Bayes are correct (multiple answers possible)?
Option 1: Naïve Bayes assumes that samples are statistically independent given the class value
This is a core aspect of Naïve Bayes: the conditional independence assumption of features given the class label. In other words, given the class, the features are treated as independent of one another. This ......Login to view full explanationLog in for full answers
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Which statements about Naive Bayes are true? (Select all that apply.)
Question16 Suppose that you are given the following set of data with three Boolean input variables x1, x2 and x3 and a single Boolean output variable y (Recall: Bayes theorem tells us: [math]) [table] x1 | x2 | x3 | y 1 | 0 | 1 | 1 1 | 1 | 1 | 1 0 | 1 | 1 | 0 1 | 1 | 0 | 0 1 | 0 | 1 | 0 0 | 0 | 0 | 1 0 | 0 | 0 | 1 0 | 0 | 1 | 0 [/table] According to naïve Bayes classifier, what is [math] ? (select one) 0.75 0.25 0.5 1 ResetMaximum marks: 1 Flag question undefined
Naïve Bayes Classifier can be used for ______________________. Select all that apply for full marks.
Which of the following statements about Naive Bayes is incorrect? 1. Features are equally important 2. Features are statistically dependent of one another given the class value 3. The model calculates the posterior probability of each class given the input features 4. Features can be nominal or numeric
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