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

A. Use the toolbar options to filter the visualization

B. Use the filter pane to apply filters to the visualization

C. Apply filters using the interactive filter dialog in the visualization

D. Define filters directly in the data source properties

A. To calculate the coefficient of determination (R-squared)

B. To identify influential outliers

C. To ensure that predictor variables are not highly correlated

D. To detect non-linear relationships

A. To identify outliers in the data

B. To capture the relationship between predictors and the response variable

C. To assess multicollinearity among predictors

D. To calculate the p-values of the predictors

A. Impurity measures represent the number of data points in a leaf node.

B. Higher impurity indicates better separation of classes.

C. Impurity measures are irrelevant in decision tree analysis.

D. Lower impurity implies more homogeneity within a leaf node.

A. Include the correct offset term.

B. Select Informative Missingness on the properties tab.

C. Include the correct interaction term.

D. Select Use Variable Selection on the properties tab.

A. Decision tree

B. Logistic regression

C. Generalized linear model

D. Linear regression

A. Maximum leaf nodes

B. Maximum depth

C. Maximum tree height

D. Minimum split count

A. To remove all leaf nodes and keep only the root node

B. To convert the decision tree into a random forest model

C. To trim branches of the tree to reduce complexity and overfitting

D. To increase the depth of the tree to improve accuracy

A. The standard deviation of the predictor variables

B. The summary statistics of the response variable

C. The distribution of residuals in the model

D. The performance metrics of the model, such as AUC and KS Statistic

A. Variable transformation

B. Binning or grouping

C. Principal Component Analysis (PCA)

D. Data imputation