Bagging is used in a number of situations, one of which is improving predictive power. Bagging is especially helpful for unstable models. This example uses the Tiretread.jmp sample data table. There are three factors (SILICA, SILANE, and SULFUR) and four responses (ABRASION, MODULUS, ELONG, and HARDNESS). First, you fit a neural network model to simultaneously predict the four response variables as a function of the three factors. Then, you perform bagging on the neural network model. Last, you compare the predictions to show the improvements obtained through bagging.
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Select Analyze > Predictive Modeling > Neural.

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Click OK.

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Click Go.

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Select Save Formulas from the red triangle menu next to Model NTanH(3).

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From the red triangle menu next to Prediction Profiler, select Save Bagged Predictions.

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Click OK.

Return to the data table. For each response variable, there are three new columns denoted as Pred Formula <colname> Bagged Mean, StdError <colname> Bagged Mean, <colname> Bagged Std Dev. The Pred Formula <colname> Bagged Mean columns are the final predictions.
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Select Analyze > Predictive Modeling > Model Comparison.

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Click OK.

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From the red triangle menu next to Model Comparison, select Plot Actual by Predicted.

The Measures of Fit report and the Actual by Predicted Plot are shown in Comparison of Predictions for ABRASION. The predictions that were obtained from bagging are shown in blue. The predictions that were obtained from the original neural network model are shown in red. In general, the bagging predictions are closer to the line than the original model predictions. Because the bagging predictions are closer to the line, the RSquare value of 0.6699 for the bagged predictions is higher than the RSquare value for the original model predictions. You conclude that bagging has improved predictions for ABRASION.
This example compared the predictions for ABRASION. To compare predictions for another response variable, follow step 2 through step 6, replacing ABRASION with the desired response variable. As another example, Comparison of Predictions for HARDNESS shows the Measures of Fit report for HARDNESS. The report shows similar findings as the Measures of Fit report for ABRASION. The RSquare value for the bagged predictions is slightly higher than the RSquare value for the original model predictions, which indicates a better fit and improved predictions.