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

Run Predictive Analytics Module > TreeNet® Classification > 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 minimum average –loglikelihood. 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 13 steps. The model with the least average –loglikelihood is the model with all of the predictors. The model at step 11 has an average –loglikelihood that is relatively close to the best value. The model at step 11 has 3 predictors. The full results from the model at step 11 are also of interest.

TreeNet® Classification - Discover Key Predictors: Acceptable P vs Flour Protei, Water, ...

Predictor Elimination Plot

Model Selection by Eliminating Unimportant Predictors Test Optimal Number Average Number of Model of Trees -Loglikelihood Predictors Eliminated Predictors 1 268 0.273936 29 None 2 268 0.274186 27 Foam Stability, Bulk Density 3 234 0.273843 26 Least Gelation Concentration 4 233 0.274350 25 Oven Mode 2 5 232 0.274943 24 Kiln Method 6 273 0.275553 23 Oven Mode 1 7 244 0.274811 22 Mix Speed 8 268 0.274258 21 Oven Mode 3 9 272 0.274185 20 Resting Surface 10 232 0.274077 19 Bake Temperature 3 11 287 0.273598 18 Mix Tool 12 227 0.274358 17 Bake Temperature 1 13 276 0.275374 16 Rest Time 14 272 0.276082 15 Water 15 268 0.275595 14 Caustic Concentration 16 268 0.277810 13 Swelling Capacity 17 253 0.276436 12 Emulsion Stability 18 231 0.276159 11 Emulsion Activity 19 268 0.273537 10 Water Absorption Capacity 20 260 0.273455 9 Oil Absorption Capacity 21 299 0.272848 8 Flour Protein 22 278 0.272629 7 Foam Capacity 23* 299 0.267184 6 Flour Size 24 297 0.288621 5 Bake Temperature 2 25 234 0.330342 4 Dry Time 26 290 0.305993 3 Gelatinization Temperature 27 245 0.534345 2 Bake Time 28 146 0.599837 1 Kiln Temperature The algorithm removed one predictor and any predictors with 0 importance at each step. * Selected model has minimum average -loglikelihood. Output for the selected model follows.

One Predictor Partial Dependence Plots

Select More Predictors to Plot...

Two Predictor Partial Dependence Plots

Select More Predictors to Plot...

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® Classification or Predict new results for Fit Model and Discover Key Predictors with TreeNet® Classification.


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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