QUESTION 3
The mathematical function in which each variable appears in a separate term and is raised to the first power is known as a
a. |
nonlinear function |
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b. |
power function |
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c. |
linear function |
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d. |
what-if function |
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e. |
None of the above |
QUESTION 5
The confusion matrix for a classification method with Class 0 and Class 1 is given below.
Confusion Matrix |
||
Actual\Predicted |
0 |
1 |
0 |
108 |
50 |
1 |
32 |
110 |
What is the percent overall error rate?
a. |
37.6% |
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b. |
8.58% |
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c. |
27.33% |
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d. |
25.90% |
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e. |
None of the above |
1.5 points
QUESTION 6
Constrains are
a. |
quantities to be maximized n a linear programming model. |
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b. |
quantities to be minimized n a linear programming model. |
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c. |
restrictions that limit the settings of the decision variables. |
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d. |
input variables that can be controlled during optimization. |
QUESTION 21
The range of computer-generated random numbers is
a. |
[-9,9] |
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b. |
[0, 1) |
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c. |
[-9, 0) |
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d. |
[1,9] |
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e. |
None of the above |
1.5 points
QUESTION 22
The confusion matrix for a classification method with Class 0 and Class 1 is given below.
Confusion Matrix |
||
Actual\Predicted |
0 |
1 |
0 |
108 |
50 |
1 |
32 |
110 |
What are the percent error rates for Class 1?
a. |
22.54% |
|
b. |
16.38% |
|
c. |
41.25% |
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d. |
36.67% |
|
e. |
None of the above |
answer all questions
Q. 3 : answer c) Linear Function
Mathematical functions in which each variable appears in a separate term and is raised to the first power are called linear functions.
Q.5. answer : c) 27.33%
Error rate is calculated as the total number of two incorrect predictions (FN + FP) divided by the total number of a dataset (P + N). overall error rate = (82/300 )*100 = 27.33%
Q.6 Answer : c) restrictions that limit the settings of the decision variables.
Q.21 answer : b. [0, 1)
Q. 22 answer : a) 22.54%
Error rate of class 1 = 100- number of correct positive predictions divided by the total number of positives*100
Error rate of class 1 = 100 - (110/142) *100
= 22.54%
PL??
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