Select the graphs to display for Random Forests® Classification

Predictive Analytics Module > Random Forests® Classification > 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.

Misclassification rate vs number of trees plot
The misclassification rate vs. number of trees plot shows the relationship between classification errors and the amount of tree. If you provide a separate test set, the plot will include two curves for the out-of-bag data and for the test set.
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 increase in importance when they split a node in any tree in the analysis.
  • 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.
Ranking method
Select how Minitab calculates the relative importance scores for the variables on the variable importance chart. For Permutation, Minitab evaluates how much worse the model performs by validating the model again with the permuted values of a variable on the chart. For Gini, Minitab sums the improvements the variable makes for all of the trees. Permutation is the default method for datasets with 5000 or fewer records. Consider whether to use Permutation for larger datasets when the analysis does not take too long and the identification of important predictors is an important goal.
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.
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