Modeling and Multivariate Methods - SAS

Chapter 25 Comparing Model Performance 635
Additional Example of Model Comparison
Lift Curve Shows lift curves for each level of the response variable. The curves for the different models are
overlaid.
Profiler Shows a profiler for each response based on prediction formula columns in your data. The
profilers have a row for each model being compared.
Related Information
• “ROC Curve” on page 337 in the “Recursively Partitioning Data” chapter
• “Lift Curves” on page 339 in the “Recursively Partitioning Data” chapter
Additional Example of Model Comparison
This example uses automobile data to build a model to predict the size of the purchased car. A logistic
regression model and a decision tree model are compared.
Begin by opening the Car Physical Data.jmp sample data table.
Create the Logistic Regression Model
1. Select Analyze > Fit Model.
2. Select Type and click Y.
3. Select the following columns and click Add: Country, Weight, Turning Cycle, Displacement, and
Horsepower.
4. Click Run.
The Nominal Logistic Fit report appears.
5. Save the prediction formulas to columns by selecting Save Probability Formula from the Nominal
Logistic red triangle menu.
Create the Decision Tree Model and Save the Prediction Formula to a Column
1. Select Analyze > Modeling > Partition.
2. Select Type and click Y, Response.
3. Select the Country, Weight, Turning Cycle, Displacement, and Horsepower columns and click X,
Factor.
4. Make sure that Decision Tree is selected in the Method list.
5. Click OK.
The Partition report appears.
6. Click Split 10 times.
7. Save the prediction formulas to columns by selecting Save Columns > Save Prediction Formula from
the Partition red triangle menu.