SAS Data Scientist ML Techniques Quiz

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3. In a ROC curve, what does the area under the curve (AUC) represent?

Model accuracy only

Probability of correct classification across all thresholds

Number of features used

Training time complexity

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5. Which SAS procedure performs variable selection for regression models?

PROC REG with SELECTION option

PROC STEP

PROC SELECT

PROC CHOOSE

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7. Which validation metric measures the proportion of correctly classified instances?

Sensitivity

Specificity

Accuracy

F1-score

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9. Principal Component Analysis (PCA) is used primarily for which purpose?

Dimensionality reduction

Classification

Regression

Time series forecasting

10. Which SAS procedure creates a neural network model for prediction?

PROC NEURAL

PROC NNET

PROC MLP

PROC DEEP

11. Which SAS procedure is primarily used for logistic regression modeling?

PROC REG

PROC LOGISTIC

PROC GENMOD

PROC GLM

12. In SAS Enterprise Miner, what is the primary purpose of the Data Partition node?

To merge multiple datasets

To split data into training, validation, and test sets

To remove duplicate records

To normalize numerical variables

13. Which ensemble method combines weak learners sequentially, where each corrects previous errors?

Bagging

Boosting

Stacking

Voting

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15. Feature scaling is important before applying which algorithm?

Decision trees

K-nearest neighbors

Random forest

All of the above

16. Which metric is best for evaluating classification models with imbalanced classes?

Accuracy

Precision and Recall

R-squared

Mean Absolute Error

17. What does 'overfitting' mean in model development?

Model performs well on training data but poorly on test data

Model performs poorly on all data

Model has too few features

Model uses linear regression only

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19. Which technique randomly selects features to split nodes in decision trees?

Bagging

Boosting

Random Forest

Gradient descent

20. What is the main purpose of cross-validation in machine learning?

To increase model bias

To estimate model performance on unseen data

To reduce training time

To eliminate all features

SAS Data Scientist Machine Learning Techniques Quiz

1. Regularization techniques like Ridge and Lasso reduce model ____ by adding penalty terms.

2.

What first name or nickname would you like us to use?

2. Handling missing values by replacing them with the mean or median is called ____ imputation.

3. In a ROC curve, what does the area under the curve (AUC) represent?

4. Support Vector Machines (SVM) find an optimal ____ that separates classes in feature space.

5. Which SAS procedure performs variable selection for regression models?

6. The ____ effect occurs when a model learns noise in the training data instead of underlying patterns.

7. Which validation metric measures the proportion of correctly classified instances?

8. In gradient boosting, the learning rate controls the ____ of each boosting iteration.

9. Principal Component Analysis (PCA) is used primarily for which purpose?

10. Which SAS procedure creates a neural network model for prediction?

11. Which SAS procedure is primarily used for logistic regression modeling?

12. In SAS Enterprise Miner, what is the primary purpose of the Data Partition node?

13. Which ensemble method combines weak learners sequentially, where each corrects previous errors?

14. The ____ matrix shows the count of true positives, false positives, true negatives, and false negatives.

15. Feature scaling is important before applying which algorithm?

16. Which metric is best for evaluating classification models with imbalanced classes?

17. What does 'overfitting' mean in model development?

18. In SAS, the PROC ____ procedure is used for k-means clustering.

19. Which technique randomly selects features to split nodes in decision trees?

20. What is the main purpose of cross-validation in machine learning?