Topic 1 Question 105
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 naive Bayesian model, since the features are all conditionally independent.
A full Bayesian network, since the features are all conditionally independent.
A naive Bayesian model, since some of the features are statistically dependent.
A full Bayesian network, since some of the features are statistically dependent.
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I would say D, because of correlations and dependencies between features. See https://towardsdatascience.com/basics-of-bayesian-network-79435e11ae7b and https://www.quora.com/Whats-the-difference-between-a-naive-Bayes-classifier-and-a-Bayesian-network?share=1
👍 23joep212021/09/20It should be D. Naive Bayes is called naive because it assumes that each input variable is independent. This is a strong assumption and unrealistic for real data; however, the technique is very effective on a large range of complex problems.
👍 7Vita_Rasta844442021/10/26D. Naive bayes - features are independent given the class.
👍 6SophieSu2021/10/17
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