Example of Boosted Tree with a Continuous Response
1.
Select Help > Sample Data and open the Body Fat.jmp sample data table.
2.
Select Analyze > Predictive Modeling > Boosted Tree.
3.
Select Percent body fat and click Y, Response.
4.
Select Age (years) through Wrist circumference (cm) and click X, Factor.
5.
Select Validation and click Validation.
6.
7.
Click OK.
Figure 6.4 Overall Statistics for Continuous Response
The Overall Statistics report provides the R-square and RMSE for the boosted tree model. The R-square for the validation set is 0.603. The RMSE for the validation set is about 5.48.
9.
Click the red triangle next to Prediction Profiler and select Assess Variable Importance > Independent Uniform Inputs.
Figure 6.5 Summary Report for Variable Importance
The Summary Report shows that Abdomen circumference (cm) is the most important predictor of Percent body fat.

Help created on 7/12/2018