Please review the following questions & answers for accuracy, and provide an explanation for the ones that are incorrect.

1.Entropy:

A)is the same thing as information gain

B)is used to calculate information gain

C)is a measure of correlation between numeric variables

D)is never used with tree induction models

2.Inductionreasons from general knowledge to specific facts. True or False? False

3.In a classification tree, a non-leaf node (or "decision node") allows us to give a class prediction. True or False? False

4.Decision trees can never provide estimates of the probability of customer churn. True or False? False

5.Decision trees can be used for both classification and regression tasks. True or False? False

6.Which of following would NOT be an example of applying supervised predictive modeling?

A)A business school that wants to start a new Master's degree program in Business Analytics, and would like to estimate the likely number of applicants.

B)A grocery store that is trying to identify which of its loyalty-card-carrying customers will spend more than $100 next month.

C)An online marketing company that wants to estimate the number of clicks that the ads it serves will receive when shown to a particular population.

D)A local think tank interested in discovering if registered voters fall into different groups

E)Decision trees are equivalent to a set of IF/THEN decision rules. True or False? True

F)When creating a decision tree, typically the root node is the attribute with the:

A)Highest information gain

B)Lowest information gain

C)Highest mean numerical value

D)Lowest entropy