For the latest version of JMP Help, visit

Predictive and Specialized Modeling > Support Vector Machines > Example of Support Vector Machines
Publication date: 11/10/2021

Image shown hereExample of Support Vector Machines

You have baseline medical data for 442 diabetic patients. You also have a binary measure of diabetes disease progression obtained one year after each patient’s initial visit. This measure quantifies disease progression as either Low or High. You want to construct a classification model to predict the disease progression for future patients as High or Low. You explore both kernel function options.

1. Select Help > Sample Data Library and open

2. Select Analyze > Predictive Modeling > Support Vector Machines.

3. Select Y Binary and click Y, Response.

4. Select Age through Glucose and click X, Factor.

5. Select Validation and click Validation.

6. Click OK.

7. In the Model Launch control panel, check that the kernel function is a Radial Basis Function and select Tuning Design.

8. Enter 10 next to Number of Runs.

9. Click Go.

10. Click the gray triangle next to Model Launch to open the Model Launch control panel.

11. Change the kernel function to a Linear function and select Tuning Design.

12. Enter 10 next to Number of Runs.

13. Click Go.

Figure 9.2 Model Comparison Report 

Model Comparison Report

The Model Comparison report shows that the best model in terms of misclassification rate and RSquare is Model 20. This model has a linear kernel function with cost parameter 0.04975. This is the model to further analyze.

Figure 9.3 Model Report for Best Fitting Model 

Model Report for Best Fitting Model

The Model Summary report shows that the misclassification rates for the training and validation sets are very similar. This is a good indication that the model did not overfit the data. The confusion matrices provide more information about the types of observations that are misclassified by the model. In the confusion matrices, the upper left corner shows that the model correctly categorizes the Low responses most of the time (96% in training and 92.6% in validation). However, fewer of the High responses are correctly categorized (53% in training and 68.4% in validation). Therefore, most of the misclassifications are the High responses being misclassified as Low.

Want more information? Have questions? Get answers in the JMP User Community (