최신 A00-485 무료덤프 - SASInstitute Modeling Using SAS Visual Statistics

A. The decision boundaries between leaf nodes

B. The feature importance for each variable in the tree

C. The tree's training and validation performance metrics

D. The tree's structural properties, such as depth and number of nodes

A. When there is no assumption of linearity in the data

B. When the goal is to model categorical outcomes

C. When dealing with count data

D. When the dependent variable is binary

A. Missing data that is randomly distributed across observations

B. Missing data that is unrelated to the outcome variable

C. Missing data that leads to multicollinearity among predictors

D. Missing data that provides valuable information about the outcome

A. The distribution of the response variable

B. The relationship between two predictor variables

C. The trade-off between false positives and false negatives at different thresholds

D. The performance of models in terms of AUC

A. Predictor

B. Input

C. Target

D. Output

A. By changing the K-value

B. By adjusting the learning rate

C. By modifying the random seed

D. By increasing the number of iterations

A. By maximizing information gain or Gini impurity reduction

B. Randomly assigned

C. By minimizing the number of leaf nodes

D. Based on the order of appearance in the dataset

A. By providing a visual interface for connecting to external data sources

B. By generating score code automatically during ETL processes

C. By automatically tuning hyperparameters for scoring

D. By enabling the scheduling of batch scoring jobs

A. Fit a model for each data segment defined by one or more category variables.

B. Use variable selection.

C. Fit a model for every value of a measure's distribution.

D. Leverage informative missingness for measure and category variables.

A. 7

B. 1

C. 3

D. 5