Create a pivot table for the training data

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Reference no: EM13758259 , Length: 4

Question 1:

Create a pivot table for the training data with Online as a column variable, CC as a row variable, and Loan as a secondary row variable.

The values inside the cells should convey the count (number of records).

Complete the numbers in the table below:

 

 

online=0

online=1

CC=0

Loan=0



CC=0

Loan=1



CC=1

Loan=0



CC=1

Loan=1



Question 2

Consider the task of classifying a customer who owns a bank credit card and is actively using online banking services. Looking at the pivot table that you created, what is the probability that this customer will accept the loan offer?

Question 3

Create two separate pivot tables for the training data. One will have Loan (rows) as a function of Online (columns) and the other will have Loan (rows) as a function of CC.

Compute the probabilities below (report three decimals).

Note: P(A|B) means "the probability of A given B".

1. P(CC = 1|Loan = 1) = the proportion of credit card holders among the loan acceptors = 

2. P(Online = 1|Loan = 1) = 

3. P(Loan = 1) = the proportion of loan acceptors = 

4. P(CC = 1|Loan = 0) = 

5. P(Online = 1|Loan = 0) = 

6. P(Loan = 0) = 

Question 4

Compute the naive Bayes probability P(Loan = 1|CC = 1, Online = 1).

Note: Use the quantities that you computed in the previous question.

Question 5

Of the two values that you computed earlier, which is a more accurate estimate of P(Loan=1|CC=1, Online=1)?

Select one:

The value based on the separate pivot tables (one with CC and Loan, and one with Online and Loan)

The value based on the complete crossed pivot table (with Online, CC, Loan)

Question 6

In XLMiner, run naive Bayes on the data and request Detail Report for the training data. Examine the "Conditional probabilities" table. Which of the entries in this table are needed for computing P(Loan = 1|CC = 1, Online = 1)? Mark all that apply (you may get slightly different but very close probabilities due to software upgrade, use the closest ones for selecting your  s.)

Select one or more:
0.301
0.402
0.374
0.288
0.712
0.699
0.598
0.626

Question 7

In the XLMiner Naive Bayes output, locate the predicted probability for P(Loan=1 | Online = 1, CC = 1). The 4-decimal value is given by...

Reference no: EM13758259

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