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

1. What is overfitting in machine learning, and how can it be addressed in a pipeline?

A) Overfitting is not a concern in machine learning pipelines.
B) Overfitting occurs when the model fits the training data too closely and may not generalize well. It can be addressed by regularization techniques.
C) Overfitting occurs when the model is too simple and underperforms.
D) Overfitting occurs when the model is too complex and overperforms.

2. Which evaluation metric is commonly used for assessing the performance of a binary classification model?

A) Accuracy
B) Mean Absolute Error (MAE)
C) R-squared
D) Root Mean Squared Error (RMSE)

3. What does the term "bagging" refer to in ensemble learning?

A) A technique that reduces model complexity
B) A form of dimensionality reduction
C) The process of combining multiple identical models to reduce variance
D) A type of feature extraction

4. What is the main goal of data preprocessing in a machine learning pipeline?

A) To visualize the data
B) To prepare the data for analysis and modeling
C) To remove irrelevant features
D) To train the model

5. What is the main advantage of ensemble methods in model building?

A) They combine multiple models to improve predictive performance
B) They produce simple and interpretable models
C) They work well with high-dimensional data
D) They require minimal data preprocessing

Solutions: