Random Forest Results - Quick Tab

Click the Summary button to display a graph of the average squared prediction error (regression analysis) or misclassification rate (classification analysis) over the successive tree addition steps; separate lines will be displayed in this graph for the training data and the testing data.

The graph will also indicate the particular number of trees that resulted in the lowest average squared error (for regression) or lowest misclassification rate (for classification). That solution is likely near the prediction model with the best predictive validity. By default, this will be the solution that is selected into the Number oftrees field in the Random Forest Results dialog.

Click this button to display a spreadsheet with risk estimates for the analysis sample and the test sample. For classification-type problems (see the description of the

Random Forests

Startup Panel -

Quick

tab) with a categorical dependent variable and equal misclassification costs (see the description of the

Random Forest Specifications

dialog -

Classification

tab), risk is calculated as the proportion of cases incorrectly classified by the trees (in the respective type of sample). If unequal misclassification costs are specified, the risk is adjusted accordingly, (expressed relative to the overall cost). For regression-type problems with a continuous dependent variable, risk is calculated as the residual variance. The standard error for the risk estimate is also reported. [See also, Breiman, et al. (1984) and General Classification and Regression Trees (GC&RT)].

Click this button to display a plot of the importance for each predictor variable in the analysis. The predictor importance is computed as follows:

During the building of each tree, for each split, predictor statistics (sums of squares regression, since simple regression trees are built in all cases) are computed for each predictor variable; the best predictor variable (yielding the best split at the respective node) will then be chosen for the actual split. The program also computes the average of the predictor statistics for all variables over all splits and over all trees. The final predictor importance values are computing by normalizing those averages so that the highest average is assigned the value of 1, and the importance of all other predictors is expressed in terms of the relative magnitudes of the average values of the predictor statistics, relative to the most important predictor.

Click the Predictor importance button to display a spreadsheet that contains the importance values and importance ranking on a 0-100 scale basis for each predictor variable in the analysis. See the description of the Barplot of predictor importance option above for additional details. See also Predictor Importance in Statistica GC&RT, Interactive Trees, and Boosted Trees.

Use the options in this group box to review details of the final solution for all categories of the dependent variable.

The method of Random Forest (see the Introductory Overview and Technical Notes) will generate a sequence of simple trees (the complexity of each tree can be specified on the

Random Forest Specifications

dialog -

Advanced

tab). If you want to review the actual individual trees (as Tree graphs or the Tree structure, see below), select here the specific numbers of trees you want to review.

Click the Tree graphs button to display the individual trees from the Random Forest, as specified via the Start of tree graphs and End of tree graphs options, see above.

Click the Tree structures button to display in results spreadsheets the structures of the individual trees from the Random Forest, as specified via the Start of tree graphs and End of tree graphs options.