A00-255無料問題集「SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14」

質問 1

Perform these tasks in SAS Enterprise Miner:
* Add a Decision Tree node, as shown below. (Make sure you use only default options in the Decision Tree node.)

* Run the Decision Tree node.
What percentage of all observations is being correctly predicted in the test data set by the decision tree?
Response:

（A）84.5212%

（B）16.8874%

（C）83.1126%

（D）85.2222%

正解：C 解答を投票する

質問 2

Which of the following is not true about results produced by the Regression node?
Response:

（A）Type 3 Analysis of Effects provides you with information about the number of parameters that each input contributes to the model.

（B）Variable Summary information identifies the roles of variables used by the Regression node.

（C）Fit Statistics can provide information that affects decision predictions, but does not affect estimate predictions.

（D）Model Information provides you with information that includes the number of target categories and the number of model parameters.

正解：C 解答を投票する

質問 3

Look over the output from the Neural Network model. Which of the following statement(s) is (are) true?
Response:

（A）The model has too few input variables.

（B）The misclassification error for the test data is 0.154255.

（C）The optimization for the model has not been completed.

（D）All of the above

正解：C 解答を投票する

質問 4

What percentage of observations in the test data has TARGET=1?
Response:

（A）16.6627

（B）16.8874

（C）83.3333

（D）16.5924

正解：B 解答を投票する

質問 5

Perform these tasks in SAS Enterprise Miner:
- Use the Regression node to build another regression model with TARGET as the dependent variable and all other input variables as independent variables (main effects only).
- Configure the regression model to use Stepwise for Selection Model and Validation Error for Selection Criteri a. Do not change any other property for the regression model.
For the validation data, in what range does cumulative percent captured response at the 60th percentile lie?
Response:

（A）25-49.99

（B）0-24.99

（C）75 or more

（D）50-74.99

正解：C 解答を投票する

質問 6

Which of the following is not a good reason to"regularize" input distributions using a simple transformation?
Response:

（A）When you perform regression, inputs with highly skewed or highly kurtotic distributions can be selected over inputs that would yield better overall predictions.

（B）Another benefit is ease in model interpretation.

（C）Regression models are sensitive to extreme or outlying values in the input space.

（D）One benefit is improved model performance.

正解：B 解答を投票する

質問 7

Transformation of input variables to make their distributions more symmetric will likely have what impact in a logistic regression?
Select one:
Response:

（A）create convergence problems in maximum likelihood estimation

（B）increase the performance of logistic regression

（C）neither increase nor decrease the performance of logistic regression

（D）decrease the performance of logistic regression

正解：B 解答を投票する

質問 8

If the bank wanted to select the best model based on which model achieves the highest lift for the third decile in the validation data, then which of the following is the best model?
Response:

（A）Decision Tree (3-way)

（B）Decision Tree

（C）Regression

（D）Neural Network

正解：B 解答を投票する

質問 9

Which method of input selection for regression analysis evaluates the statistical significance of all included inputs after each input is added?
Select one:
Response:

（A）Backward

（B）Simple

（C）Forward

（D）Stepwise

正解：D 解答を投票する

A00-255 無料問題集「SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14」

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