independent<\/strong>.<\/p>\nIn Naive Bayes, features are assumed to be independent of each other. This means that the probability of a particular class label given a set of features is the product of the probabilities of each feature given the class label. This assumption is often not true in real-world data, but it can still be a useful approximation.<\/p>\n
If features are dependent, then the probability of a particular class label given a set of features is not the product of the probabilities of each feature given the class label. This is because the value of one feature can affect the value of another feature. For example, if we are trying to classify a person’s age, the value of the feature “height” may be affected by the value of the feature “weight”.<\/p>\n
If features are partially dependent, then the probability of a particular class label given a set of features is somewhere between the product of the probabilities of each feature given the class label and the probability of the class label given all of the features. This is because the value of one feature can affect the value of another feature, but the effect is not as strong as in the case of dependent features.<\/p>\n
In conclusion, the correct answer is: independent<\/strong>.<\/p>\n","protected":false},"excerpt":{"rendered":"[amp_mcq option1=”independent” option2=”dependent” option3=”partial dependent” option4=”none” correct=”option1″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[729],"tags":[],"class_list":["post-58967","post","type-post","status-publish","format-standard","hentry","category-machine-learning","no-featured-image-padding"],"yoast_head":"\n
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