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Question 178 - MLS-C01 discussion

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A Machine Learning Specialist is deciding between building a naive Bayesian model or a full Bayesian network for a classification problem. The Specialist computes the Pearson correlation coefficients between each feature and finds that their absolute values range between 0.1 to 0.95.

Which model describes the underlying data in this situation?

A.
A naive Bayesian model, since the features are all conditionally independent.
Answers
A.
A naive Bayesian model, since the features are all conditionally independent.
B.
A full Bayesian network, since the features are all conditionally independent.
Answers
B.
A full Bayesian network, since the features are all conditionally independent.
C.
A naive Bayesian model, since some of the features are statistically dependent.
Answers
C.
A naive Bayesian model, since some of the features are statistically dependent.
D.
A full Bayesian network, since some of the features are statistically dependent.
Answers
D.
A full Bayesian network, since some of the features are statistically dependent.
Suggested answer: D

Explanation:

A naive Bayesian model assumes that the features are conditionally independent given the class label. This means that the joint probability of the features and the class can be factorized as the product of the class prior and the feature likelihoods. A full Bayesian network, on the other hand, does not make this assumption and allows for modeling arbitrary dependencies between the features and the class using a directed acyclic graph. In this case, the joint probability of the features and the class is given by the product of the conditional probabilities of each node given its parents in the graph. If the features are statistically dependent, meaning that their correlation coefficients are not close to zero, then a naive Bayesian model would not capture these dependencies and would likely perform worse than a full Bayesian network that can account for them. Therefore, a full Bayesian network describes the underlying data better in this situation.References:

Naive Bayes and Text Classification I

Bayesian Networks

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asked 16/09/2024
Mahendra Belgaonkar
36 questions
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