Select the correct statement regarding the naive Bayes classification:

A. it only requires a small amount of training data to estimate the parameters

B. Independent variables can be assumed

C. only the variances of the variables for each class need to be determined

D. for each class entire covariance matrix need to be determined

You are working in an ecommerce organization, where you are designing and evaluating a recommender system, you need to select which of the following metric wilt always have the largest value?

A. Root Mean Square Error

B. Sum of Errors

C. Mean Absolute Error

D. Both land 2

E. Information is not good enough.

In which of the following scenario we can use naTve Bayes theorem for classification

A. Classify whether a given person is a male or a female based on the measured features. The features include height, weight and foot size.

B. To classify whether an email is spam or not spam

C. To identify whether a fruit is an orange or not based on features like diameter, color and shape

Suppose A, B , and C are events. The probability of A given B , relative to P(|C), is the same as the probability of A given B and C (relative to P ). That is,

A. P(A,B|C) P(B|C) =P(A|B,C)

B. P(A,B|C) P(B|C) =P(B|A,C)

C. P(A,B|C) P(B|C) =P(C|B,C)

D. P(A,B|C) P(B|C) =P(A|C,B)

You are creating a Classification process where input is the income, education and current debt of a customer, what could be the possible output of this process?

A. Probability of the customer default on loan repayment

B. Percentage of the customer loan repayment capability

C. Percentage of the customer should be given loan or not

D. The output might be a risk class, such as "good", "acceptable", "average", or "unacceptable".