Example of Discover Key Predictors with TreeNet^® Classification

Interpret the results

For this analysis, Minitab Statistical Software compares 28 models. The number of steps is less than the maximum number of steps because the Foam Stability predictor has an importance score of 0 in the first model, so the algorithm eliminates 2 variables in the first step. The asterisk in the Model column of the Model Evaluation table shows that the model with the smallest value of the average –loglikelihood statistic is model 23. The results that follow the model evaluation table are for model 23.

Although model 23 has the smallest value of the average –loglikelihood statistic, other models have similar values. The team can click Select Alternative Model to produce results for other models from the Model Evaluation table.

In the results for Model 23, the Average –Loglikelihood vs. Number of Trees Plot shows that the optimal number of trees is almost the number of trees in the analysis. The team can click Tune Hyperparameters to increase the number of trees and to see whether changes to other hyperparameters improve the performance of the model.

The Relative Variable Importance graph plots the predictors in order of their effect on model improvement when splits are made on a predictor over the sequence of trees. The most important predictor variable is Mix Time. If the importance of the top predictor variable, Mix Time, is 100%, then the next important variable, Kiln Temperature, has a contribution of 93.9%. This means that Kiln Temperature is 93.9% as important as Mix Time.

Use the partial dependency plots to gain insight into how the important variables or pairs of variables affect the fitted response values. The fitted response values are on the 1/2 log scale. The partial dependence plots show whether the relationship between the response and a variable is linear, monotonic, or more complex.

The one predictor partial dependence plots show that medium values for Mix Time, Kiln Temperature and Bake Time increase the odds of an acceptable pretzel. A medium value of Dry Time decreases the odds of an acceptable pretzel. The researchers can select One-Predictor Plots to produce plots for other variables.

The two-predictor partial dependence plot of Mix Time and Kiln Temperature shows a more complex relationship between the two variables and the response. While medium values of Mix Time and Kiln Temperature increase the odds of an acceptable pretzel, the plot shows that the best odds occur when both variables are at medium values. The researchers can select Two-Predictor Plots to produce plots for other pairs of variables.