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

1. In model evaluation, what is the purpose of a ROC curve (Receiver Operating Characteristic)?

A) To visualize data distribution
B) To compare models' performance in terms of sensitivity and specificity
C) To measure feature importance
D) To evaluate the mean squared error of a model

2. What is the primary purpose of model assessment in the context of data science and machine learning?

A) Data preprocessing
B) Model building
C) Evaluating and selecting the best-performing model
D) Data visualization

3. What does "feature selection" refer to in the context of model building?

A) The process of choosing the most relevant variables (features) for the model
B) The evaluation of model accuracy
C) The creation of synthetic features from existing data
D) The visualization of data distribution

4. Which data source allows for real-time data streaming and processing?

A) IoT devices
B) Static data files
C) Cloud storage
D) Data warehouses

5. Which statement is true regarding decision trees and models based on ensembles of trees?

A) In the Forest algorithm, each individual tree is pruned based on using minimum Average Squared Error.
B) In the gradient boosting algorithm, for all but the first iteration, the target is the residual from the previous decision tree model.
C) A single decision tree will always be outperformed by a model based on an ensemble of trees.
D) For a Forest model, the out-of-bag sample is simply the original validation data set from when the raw data partitioning took place.

Solutions: