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QUESTION NO: 1
A predictive model uses a data set that has several variables with missing values.
What two problems can arise with this model? (Choose two.)
A. New cases with missing values on input variables cannot be scored without extra data processing.
B. Complete case analysis means that fewer observations will be used in the model building process.
C. The model will likely be overfit.
D. There will be a high rate of collinearity among input variables.
Answer: A,B
QUESTION NO: 2
Refer to the confusion matrix:
Calculate the accuracy and error rate (0 - negative outcome, 1 - positive outcome)
A. Accuracy = 83/150, Error Rate = 67/150
B. Accuracy = 58/102, Error Rate = 23/48
C. Accuracy = 25/150, Error Rate = 44/150
D. Accuracy = 83/102, Error Rate = 67/102
Answer: B
QUESTION NO: 3
Which statistic, calculated from a validation sample, can help decide which model to use for prediction of a binary target variable?
A. Chi Square
B. Adjusted R Square
C. Mallow's Cp
D. Average Squared Error
Answer: D
QUESTION NO: 4
In order to perform honest assessment on a predictive model, what is an acceptable division between training, validation, and testing data?
A. Training: 0% Validation: 100% Testing: 0%
B. Training: 50% Validation: 50% Testing: 0%
C. Training: 50% Validation: 0% Testing: 50%
D. Training: 100% Validation: 0% Testing: 0%
Answer: B
QUESTION NO: 5
This question will ask you to provide missing code segments.
A logistic regression model was fit on a data set where 40% of the outcomes were events (TARGET=1) and
60% were non-events (TARGET=0). The analyst knows that the population where the model will be deployed has 5% events and 95% non-events. The analyst also knows that the company's profit margin for correctly targeted events is nine times higher than the company's loss for incorrectly targeted non-event.
Given the following SAS program:
What X and Y values should be added to the program to correctly score the data?
A. X=40, Y=10
B. X=.10, Y=05
C. X=.05, Y=10
D. X=.05, Y=.40
Answer: C
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Updated: May 28, 2022