Question 1

You are required to use PROC NEURAL in SAS to construct a neural network model that minimizes overfitting. Which combination of options would be most effective for this purpose?

Options :

A : train=breiman; valdata=myvalid;

B : train=quikprop; hides=2(3);

C : decay=0.001; maxiter=200; train=conjugate;

D : train=resilient; maxiter=500; ridging=0.01;

Answer: C

Question 2

You are building a predictive model using a dataset that contains numerous variables. Upon inspection, you notice a significant number of them are highly correlated. Which of the following actions is the MOST appropriate to address potential issues with irrelevant or redundant variables before proceeding with model building?

Options :

A : Remove all variables with a correlation coefficient above 0.8 to eliminate redundancy.

B : Use a dimensionality reduction technique like Principal Component Analysis (PCA).

C : Scale all variables to have zero mean and unit variance without considering their correlations.

D : Increase the model complexity to account for the additional information in the correlated variables.

Answer: B

Question 3

A binary classifier is used to predict a rare event. Its performance is summarized in the following confusion matrix: | | Predicted Negative | Predicted Positive | |-||| | Actual Negative | 9750 | 250 | | Actual Positive | 25 | 75 | Given the model's performance, what is the False Positive Rate (FPR) of the classifier?

Options :

A : 0.0256

B : 0.03

C : 0.25

D : 0.0025

Answer: A

Question 4

A data analyst is using PROC LOGISTIC to perform logistic regression on a dataset where the response variable is binary. The analyst wants to ensure that the output dataset contains the predicted probabilities for the event of interest along with the total number of events and non-events for each group defined by the CLASS variable. Which combination of statements and options should the analyst use?

Options :

A :

CLASS treatment / DESCENDING; MODEL event(binary) = treatment; OUTPUT OUT=pred_data PREDICTED=probabilities;

B :

CLASS treatment; MODEL event(event='1') = treatment / LINK=LOGIT; OUTPUT OUT=pred_data PREDICTED=probabilities;

C :

CLASS treatment; MODEL event(event='1') = treatment / LINK=LOGIT; OUTPUT OUT=pred_data PREDICTED=probabilities EVENTCOUNT=nwayevents NONEVENTCOUNT=nwaynonevents;

D :

CLASS treatment; MODEL event(binary) = treatment; OUTPUT OUT=pred_data PREDICTED=probabilities / NWAY;

Answer: C

Question 5

You have built a logistic regression model using PROC LOGISTIC to predict the probability of customer churn based on several predictors. You want to evaluate the performance of the model by analyzing the Receiver Operating Characteristic (ROC) curve. To score a validation dataset and produce the ROC curve, which statement correctly implements the OUTROC option in the SCORE statement?

Options :

A :

SCORE DATA=validation OUT=scored_results OUTROC=roc_curve;

B :

PROC LOGISTIC DATA=training; SCORE OUTROC=roc_curve; RUN;

C :

PROC LOGISTIC DATA=training; SCORE DATA=validation OUTROC=roc_curve; RUN;

D :

SCORE DATA=validation OUT=scored_results; MODEL OUTROC=roc_curve;

Answer: C

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