Select an Alternative Model from Discover Key Predictors with TreeNet® Regression

Run Predictive Analytics Module > TreeNet® Regression > Discover Key Predictors. Click the Select an Alternative Model button after the Predictor Elimination table.

This command is available with the Predictive Analytics Module. Click here for more information about how to activate the module.


When you use Discover Key Predictors to remove the least important predictors, Minitab Statistical Software produces results for the model with the best value of the accuracy criterion for the analysis, such as the maximum R2 value. Minitab lets you explore other models from the sequence that led to the identification of the optimal model. Generally, you select an alternative model if another model has a value of the criterion close to the best, but with fewer predictors. A model with fewer predictors is easier to interpret, can have better prediction accuracy, and allows you work with a smaller number of predictors.

For example, the following model selection table has 20 steps. The model with the maximum R2 value has 5 predictors and occurs at step 16. The model at step 17 has an R2 value that is lower by less than 0.1. The model at step 17 has 4 predictors. The full results from the model at step 17 are also of interest.

TreeNet® Regression - Discover Key Predictors: Strength vs Injection Pr, Injection Te, ...

Predictor Elimination Plot

Model Selection by Eliminating Unimportant Predictors Test Optimal Number R-squared Number of Model of Trees (%) Predictors Eliminated Predictors 1 300 89.32 21 None 2 300 89.34 19 Plastic Flow Rate, Change Position 3 300 89.39 18 Drying Temperature 4 300 89.46 17 Melt Temperature Zone 2 5 300 89.51 16 Plastic Temperature 6 300 89.50 15 Formula 7 300 89.59 14 Hold Pressure 8 300 89.57 13 Screw cushion 9 300 89.69 12 Melt Temperature Zone 4 10 300 89.70 11 Back Pressure 11 300 89.86 10 Melt Temperature Zone 1 12 300 89.90 9 Drying Time 13 300 89.92 8 Temperature at Measurement 14 300 90.06 7 Melt Temperature Zone 5 15 300 90.16 6 Melt Temperature Zone 3 16* 300 90.23 5 Screw Rotation Speed 17 300 89.96 4 Injection Temperature 18 297 79.37 3 Cooling Temperature 19 244 66.64 2 Injection Pressure 20 164 46.19 1 Machine The algorithm removed one predictor and any predictors with 0 importance at each step. * Selected model has maximum R-squared. Output for the selected model follows.

One Predictor Partial Dependence Plots

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Two Predictor Partial Dependence Plots

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Perform the analysis

Click Select an Alternative Model in the output. A dialog box opens that shows a plot of the criterion against the number of eliminated predictors and a table that summarizes the steps.

Compare the criteria

To select an alterative model, click a point on the graph or a row in the table. Press Display results to create the results for that model.

Once you display the results, you can click a button in the output to tune the hyperparameters of the model or to make predictions from the model. For more information, go to Select hyperparameter values to evaluate for Fit Model and Discover Key Predictors with TreeNet® Regression or Predict new results for Fit Model and Discover Key Predictors with TreeNet® Regression.


To compare the output of two different analyses or reports, right-click the second item in the Navigator and choose Open in Split View.

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