최신SASInstitute Modeling Using SAS Visual Statistics - A00-485무료샘플문제

A. Principal Component Analysis (PCA)

B. K-means

C. Decision Tree

D. Linear Regression

A. If they have similar AUC values

B. If they have different learning rates

C. If they are both logistic regression models

D. If they use the same dataset and response variable

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

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

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

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

A. SAS Enterprise Guide

B. SAS Visual Data Builder

C. SAS Studio

D. SAS Enterprise Miner

A. Maximum tree height

B. Maximum leaf nodes

C. Minimum split count

D. Maximum depth

A. Apply group-by modeling to multiple measures simultaneously

B. Create subgroups within the group-by variable

C. Dynamically change the group-by variable during analysis

D. Interactively explore different levels of the group-by variable

A. The mean value of the target variable for each group

B. The distribution of the group-by variable

C. The standard deviation of the target variable for each group

D. The number of observations in each group

A. Poisson regression is a special case of negative binomial regression.

B. Negative binomial models accommodate negative integers while Poisson regression does not.

C. Poisson regression models are used for count data, and negative binomial models are used for binary responses.

D. The canonical link function for Poisson regression is the log, while for negative binomial it is the logit.

A. The distribution of the response variable

B. The correlation between predictor variables

C. The relative performance of the model compared to a random model

D. The interaction effects between predictor variables

A. Randomly assigned

B. Based on the order of appearance in the dataset

C. By minimizing the number of leaf nodes

D. By maximizing information gain or Gini impurity reduction

A. Dragging and dropping the score code file into the project

B. Running a SQL query to generate predictions

C. Opening the code file and manually executing it

D. Converting the code to a macro for automation

A. An ordinal logistic regression is performed.

B. A multinomial logistic regression is performed.

C. The other levels are offset to account for exposure.

D. The other levels are grouped into one non-event.