naive bayes

How do we perform Bayesian classification when some features are missing?

• We assuming the missing values as the mean of all values.

• We ignore the missing features.

• We integrate the posteriors probabilities over the missing features.

• Drop the features completely.

True or False: In a naive Bayes algorithm, when an attribute value in the testing record has no example in the training set, then the entire posterior probability will be zero.

• True

• False

• Can’t determined

Which of the following statement is TRUE about the Bayes classifier?

• Bayes classifier works on the Bayes theorem of probability.

• Bayes classifier is an unsupervised learning algorithm.

• Bayes classifier is also known as maximum apriori classifier.

• It assumes the independence between the independent variables or features.