Question 1

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 2

You have developed a predictive model using a very large set of independent variables. Despite achieving outstanding performance on your training dataset, when you apply your model to new, unseen data, the performance significantly deteriorates. Which of the following is the most likely explanation for this occurrence?

Options :

A :

Underfitting due to insufficient model complexity

B :

Overfitting due to the model being too complex and capturing noise in the training data

C :

Data leakage, where information from outside the training dataset has been incorrectly included

D :

The new data has a different distribution than the training data, which is an instance of dataset shift

Answer: B

Question 3

You are building an artificial neural network model to predict the probability that a customer will respond to a marketing campaign. The target variable is binary, indicating a '1' for a response and '0' for no response. Which combination of activation function and error function would be most appropriate for the output layer of this neural network?

Options :

A :

Activation function: Hyperbolic tangent (tanh), Error function: Cross-entropy

B :

Activation function: Softmax, Error function: Mean squared error (MSE)

C :

Activation function: Sigmoid, Error function: Cross-entropy

D :

Activation function: ReLU (Rectified Linear Unit), Error function: Mean absolute error (MAE)

Answer: C

Question 4

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 5

You are performing predictive modeling using a random forest technique in R through SAS Enterprise Miner. You want to examine variable importance to interpret the model. Which of the following commands within the R code can provide you with the variable importance measure typically associated with a random forest model?

Options :

A :

randomForest::importance(model)

B :

rpart::varImp(model)

C :

caret::varImp(model, scale = FALSE)

D :

varImpPlot(model)

Answer: A

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