Select the graphs that you want to display for the analysis.
The tree diagram shows the optimal tree. You can right-click the diagram to switch between the detailed and node split view. The detailed view of the tree includes the categories and counts, and the node split view shows a high level view of the model with only the variable used at each node.
- Misclassification cost vs. number of terminal
The misclassification cost vs. number
of terminal nodes plot shows the relationship between classification errors and tree size. You can select other trees to display in the tree diagram by selecting a tree with a different number of nodes.
- 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 and surrogate 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 tree to distinguish between classes. The ROC curve plots the true positive rate (TPR) against the false positive rate (FPR).
The cumulative gain chart illustrates the effectiveness of the model in a portion of the population. The gain chart plots % class versus % population.
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.