Data Warehouse Developer: 1098077

Question 1

True

Question 2

True

Question 3

0.78

Question 4

0.68

Question 5

0.33

Question 6

0.23

Question 7

0.28

Question 8

55%

Question 9

40000

Question 10

39600

Question 11

12870

Question 12

26730

Question 13

0.32

Question 14

0.33

Question 15

Type 2 Error

Question 16

Type 1 Error

Question 17

N_1,1

Question 18

Question 19

All answers are correct

Question 20

Question 21

N_1,2

Question 22

N_2,2

Question 23

N_2,1

Question 24

Question 25

Either a. or b.

Question 26

True

Question 27

False

Question 28

True

Question 29

False

Question 30

All answers are correct

Question 31

Validation data set

Question 32

All the statements are correct

Question 33

Both a. and b.

Question 34

All the answers are correct

Question 35

All the answers are correct

Question 36

All the answers are correct

Question 37

• In the K-Nearest Neighbor method, high values of k provide more smoothing that reduce the risk of overfitting due to noise in the training data, less noise, but may miss local structure.
• In  K-Nearest Neighbor method, low values of k(1, 2, 3, …) capture local structure in data, but also quite susceptible to noise.
• The performance of K-Nearest Neighbor method may suffer from the phenomenon of the curse of dimensionality.

Question 38

a. matches 2. RMSE

b. matches 1. Average Error

c. matches 5. Total SSE

d. matches 3. MAE

e. matches MAPE