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

Predictive Analytics Module > TreeNet® Classification > Fit Model > Graphs

Predictive Analytics Module > TreeNet® Classification > Discover Key Predictors > Graphs

Note

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

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

The available plot depends on the criterion that you choose to select the optimal number of trees. The plot shows the relationship between the criterion and the number of trees.
  • Average –loglikelihood vs number of trees plot
  • Area under ROC curve vs number of trees plot
  • Misclassification rate vs number of trees plot
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, click Select More Predictors to Plot underneath the one-predictor plots 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, click Select More Predictors to Plot underneath the two-predictor plots 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.
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