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SASInstitute SAS® Viya® Supervised Machine Learning Pipelines Sample Questions:

1. What is "model reevaluation" in the model deployment phase?

A) The periodic assessment of a deployed model's performance and potential retraining
B) The process of data preprocessing
C) The process of selecting features
D) The evaluation of data distribution

2. In a machine learning pipeline, what is the purpose of cross-validation?

A) To visualize the data distribution
B) To train multiple models on different subsets of the data to assess generalization
C) To split the dataset into training and testing sets
D) To evaluate the model's performance on new data

3. Which metric is commonly used to evaluate the performance of a regression model?

A) Precision
B) Mean Absolute Error (MAE)
C) F1 Score
D) Confusion Matrix

4. In the context of model building, what is the purpose of hyperparameter tuning?

A) Selecting the most important features
B) Training the model
C) Visualizing data
D) Optimizing the model's hyperparameters for better performance

5. What is the difference between a classification problem and a regression problem in machine learning?

A) There is no difference; the terms are used interchangeably.
B) Regression predicts categorical outcomes, while classification predicts numeric outcomes.
C) Classification predicts categorical outcomes, while regression predicts numeric outcomes.
D) Classification is a type of regression problem.

Solutions: