1.

Create a column called validation.

2.

On the Column Info window, select Random from the Initialize Data list.

3.

Select the Random Indicator radio button.

4.

Click OK.

1.

Select Analyze > Fit Model.

2.

3.

4.

Select Stepwise in the Personality list.

5.

6.

Click the Run button.

7.

Select Pvalue Threshold from the Stopping Rule list.

8.

Click the Go button.

9.

Click the Run Model button.

10.

Save the prediction formula to a column by selecting Save Columns > Prediction Formula on the Response red triangle menu.

1.

Select Analyze > Modeling > Partition.

2.

3.

4.

5.

Select Bootstrap Forest in the Method list.

6.

Click OK.

7.

Select the Early Stopping check box.

8.

Select the Multiple Fits over number of terms check box.

9.

Click OK.

10.

Save the prediction formula to a column by selecting Save Columns > Save Prediction Formula on the Bootstrap Forest red triangle menu.

1.

Select Analyze > Modeling > Model Comparison.

2.

Select the two prediction formula columns and click Y, Predictors.

3.

4.

Click OK.

•

Introduction to Fitting Models in the Fitting Linear Models book

To launch the Model Comparison platform, select Analyze > Modeling > Model Comparison.
For a categorical response with k levels, most model fitting platforms save k columns to the data table, each predicting the probability for a level. All k columns need to be specified as Y, Predictors. For platforms that do not save k columns of probabilities, the column containing the predicted response level can be specified as a Y, Predictors column.
If you do not specify any Y, Predictors columns, JMP uses the prediction formula columns in the data table that have either the Predicting or Response Probability column property.
The other role buttons are common among JMP platforms. See the Using JMP book for details.