Select the graphs to display for Fit Model and Discover Key Predictors with TreeNet^® Classification

Select the graphs that you want to display for the analysis.

Variable importance chart

The variable importance chart shows the relative importance of the predictors. You can choose whether to display all or some of the important variables. Variables are important when they are used as primary splitters.

• Display all important variables: By default, this chart displays all important variables.
• Display a percentage of important variables: Specify the percentage of important variables to display. Enter a value between 0 and 100.
• Display all predictor variables: Display all predictors whether or not they are important variables.

Receiver operating characteristic (ROC) curve

The receiver operating characteristic (ROC) curve shows the ability of a model to distinguish between classes. The ROC curve plots the true positive rate (TPR) against the false positive rate (FPR).

Gain chart

The cumulative gain chart illustrates the effectiveness of the model in a portion of the population. The gain chart plots the true positive rate in percent versus % population.

Lift chart

The lift chart illustrates the effectiveness of the predictive model. The chart plots cumulative lift versus % population and displays the difference between results obtained with and without the predictive model. You can specify Cumulative or Not cumulative for the lift chart.

Boxplot of event probabilities

For a binary response, the boxplot of event probabilities displays the distribution of event probabilities for both the test and training data.

One-predictor partial dependence plot for top K important variables, K =

The one predictor partial dependence plots display the fitted half log odds values for the top 4 important variables, by default. You can increase or decrease the number of important variables to plot. After you have results, select One-Predictor Plots in the results to show plots for more predictors.

Two-predictor partial dependence plot for top K important variables, K =

The two predictor partial dependence plots display the fitted half log odds values for the top 2 important variables, by default. You can increase or decrease the number of important variables to plot. After you have results, select Two-Predictor Plots in the results to show plots for more pairs of predictors.

For plots with categorical predictors, Minitab plots a scatterplot of the fitted values. For continuous predictors, you can specify Surface, Contour plots, or both.