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SASInstitute Modeling Using SAS Visual Statistics Sample Questions:

1. How are predictions formulated for a decision tree when a new data point is presented to the model?

A) By averaging the values of all leaf nodes
B) By using a weighted sum of all decision rules in the tree
C) By following a path through the tree to a leaf node and returning the majority class or mean value of that node
D) By calculating the Euclidean distance to all tree nodes and selecting the closest one

2. What is one potential disadvantage of excessively deep decision trees?

A) Increased interpretability
B) Overfitting to the training data
C) Faster prediction times
D) Greater stability in the model

3. Which modeling technique automatically includes missing values?

A) Logistic regression
B) Decision tree
C) Generalized linear model
D) Linear regression

4. Cluster Analysis in SAS Visual Statistics is categorized as which type of machine learning?

A) Unsupervised learning
B) Supervised learning
C) Reinforcement learning
D) Semi-supervised learning

5. In SAS Visual Statistics, how can you use a derived cluster ID variable in another visualization?

A) By linking it to a table in a database
B) By including it as a dimension in a new visualization
C) By embedding it within a scatter plot
D) By exporting it as a separate dataset

Solutions: