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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Building Predictive Models | 35–40% | - Understand predictive modeling concepts - Build models using neural networks - Build models using regression techniques - Build models using decision trees |
| Topic 2: Pattern Analysis | 10–15% | - Identify clusters and segments - Interpret pattern discovery results |
| Topic 3: Predictive Model Assessment and Implementation | 25–30% | - Score and deploy models - Apply appropriate fit statistics - Evaluate performance via profit/loss and comparison - Adjust for oversampling and sampling methods |
| Topic 4: Data Sources | 20–25% | - Modify and prepare source data for modeling - Explore and assess data sources - Create data sources from SAS tables |
1. Perform these tasks in SAS Enterprise Miner:
* Continue to use the same diagram. Define and create the data set CREDIT_SCORE for scoring. The variables (their roles and measurement levels) in the CREDIT_SCORE data should be set as identical to those in the CREDIT dat a. The only exception is that the scoring data does not have a TARGET variable.
* Find the best model out of Decision Tree, Decision Tree (3-way), Regression, and Neural Network as defined by each of the four model's overall performance in the validation data measured by average squared error. Now, use this best model to score the CREDIT_SCORE data.
CREDIT SCORE:
The distribution of the predicted probabilities of TARGET=0 in the scoring data is approximately which of the following?
Response:
A) bimodal
B) left skewed
C) right skewed
D) normal
2. Consider a binary target variable. Assume Accuracy is the desired assessment measure. Accuracy is not an option in the Decision Tree node. Which assessment measure can you use as a proxy for accuracy?
Select one:
Response:
A) Average Squared Error
B) Total Profit
C) Mean Square Error
D) 1 - Misclassification Rate
3. 1. Create a project named Insurance, with a diagram named Explore.
2. Create the data source, DEVELOP, in SAS Enterprise Miner. DEVELOP is in the directory c:\workshop\Practice.
3. Set the role of all variables to Input, with the exception of the Target variable, Ins (1= has insurance, 0= does not have insurance).
4. Set the measurement level for the Target variable, Ins, to Binary.
5. Ensure that Branch and Res are the only variables with the measurement level of Nominal.
6. All other variables should be set to Interval or Binary.
7. Make sure that the default sampling method is random and that the seed is 12345.
What is the mean credit card balance (CCBal) of the customers with a variable annuity?
Response:
A) $9,586.55
B) $0.00
C) $8,711.65
D) $11,142.45
4. Which model was picked as the best model by SAS Enterprise Miner?
Response:
A) Decision Tree
B) Decision Tree (3-way)
C) Regression
D) None of the above
5. For the variable TLCnt24, apply a Max Normal transformation. What transformation was selected by SAS Enterprise Miner?
Response:
A) Log
B) Square Root
C) Exponential
D) Square
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: D | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: B |
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