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.

Which of the following true with regards to the K-Means clustering algorithm?

A. Labels are not pre-assigned to each objects in the cluster.

B. Labels are pre-assigned to each objects in the cluster.

C. It classify the data based on the labels.

D. It discovers the center of each cluster.

E. It find each objects fall in which particular cluster

What is the best way to evaluate the quality of the model found by an unsupervised algorithm like k-means clustering, given metrics for the cost of the clustering (how well it fits the data) and its stability (how similar the clusters are across multiple runs over the same data)?

A. The lowest cost clustering subject to a stability constraint

B. The lowest cost clustering

C. The most stable clustering subject to a minimal cost constraint

D. The most stable clustering

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".