Select the graphs that you want to display for the analysis.
- Misclassification rate vs number of
- 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
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.
- Select how Minitab calculates the relative importance scores for the
variables on the variable importance chart. For
Minitab evaluates how much worse the model performs by validating the model
again with the permuted values of a variable on the chart. For
Minitab sums the improvements the variable makes for all of the trees.
is the default method for datasets with 5000 or fewer records. Consider whether
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).
- 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.
- 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
for the lift chart.