Workspace Node: Advanced Classification CHAID - Results - Quick Tab
In the Advanced Classification CHAID workspace node dialog box, under the Results heading, select the Quick tab to access the following options.
| Element Name | Description |
|---|---|
| Manager | |
| Grow tree | Select this option button to automatically grow the current tree, using all current settings. After growing the tree, use any of the Review tree options described below to further review the tree. You can also prune the tree using the various methods and options available in this dialog box. |
| Grow tree 1 level | Select this option button to grow the tree one level down from each of the current terminal nodes. Note that branches will only be grown if this is consistent with the current parameter settings for the current method for growing the tree (e.g., stopping rules as specified on the Stopping tab). |
| Tree view | |
| Tree browser | Select this check box to produce a complete representation of the results tree inside a Statistica Workbook-like browser, where every node is represented by a graph containing the respective split rule (unless the respective node is a terminal node) and various summary statistics. Intermediate and terminal nodes will be shown in the browser with different symbols: This browser provides a complete summary of the results and enables you to efficiently review even the most complex trees [see also, for example, the General Classification and Regression Trees (G&CRT) topic Reviewing Large Trees: Unique Analysis Management Tools]. The results displayed will differ, depending upon whether the selected response variable is categorical or continuous. |
| Results for categorical dependent variable (classification) | Clicking on a node in the tree browser will produce a graph displaying the number of cases in each category of the variable as well as the histogram of statistics for the selected node. |
| Tree layout | Select this check box to produce a graph showing the structure of the current tree. Each node will be presented as a rectangular box; terminal nodes are highlighted in red and non-terminal nodes are highlighted in blue. |
| Tree graph | Select this check box to produce a Tree graph for the current tree. In this graph, each node will be presented as a rectangular box; the terminal nodes are highlighted in red, and the intermediate nodes are highlighted in blue (by default). The following information is usually summarized in this graph: Node ID, the node size, the selected category of the response and the histogram. The graph also contains splitting information for the intermediate nodes - the splitting criterion that created its child nodes and the name of the predictor that was used in the splitting criterion. Note that all labels and legends for the graph are produced as custom text and can be edited, moved, or deleted via the Graph Options dialog box. |
| Scrollable tree | Select this check box to display the same Tree graph, but in a scrollable window. This option will produce a very large graph that can be reviewed (scrolled) behind a (resizable) window. Note that all standard graphics editing, zooming, etc., tools for customization and reviewing further details of the graph are available for this method of display. |
| Advanced scrollable tree | Select this check box to create a scrollable tree with advanced features:
|
| Tree structure | Select this check box to produce the Tree Structure spreadsheet, which contains summary information for all splits and the terminal nodes for the current tree. The information available in the tree structure will include for each node:
The node IDs of child nodes to which cases or objects are sent, depending on whether they satisfy (left branch), or do not satisfy (right branch) the split condition at a split node. The number of cases or objects belonging to the node. Information detailing the Split condition for a split node. Note that no child nodes or split conditions are displayed for the terminal nodes of the tree. In addition to the information described above, the tree structure will include the number of cases or objects in each observed class that are sent to the node. |
| Terminal nodes | Select this check box to produce a spreadsheet containing summary information for the terminal nodes only.
The spreadsheet shows the number of cases or objects in each observed class that are sent to the node; a Gain value is also reported. By default (with Profit equal to 1.0 for each dependent variable class; see also the options available on the Classification tab), the gain value is simply the total number of observations (cases) in the respective node. If separate Profit values are specified for each dependent variable class, the Gain value is computed as the total profit (number of cases times respective profit values). |
| Importance | Select the
Importance check box with the spreadsheet
 icon to produce a spreadsheet that contains the importance ranking on a 0-100 scale for each predictor variable in the analysis. Computational details regarding this measure can be found in Breiman (1984; p. 147). In general, with the results presented in this spreadsheet, you can judge the relative importance of each predictor variable for producing the final tree. Refer to the discussion in Breiman (1984) for details. See also, Predictor Importance in Statistica GC&RT, Interactive Trees, and Boosted Trees.
|
| Importance |
Select the
Importance check box with the plot
|
| Risk estimates |
Select this check box to produce a spreadsheet with risk estimates for the analysis sample, the test sample (if one is specified on the Specifications - Validation tab), and the v-fold cross-validation risk (if v-fold cross-validation is specified on the Validation tab). |
| Options / C / W | See Common Options. |
| OK | Click this button to accept all the specifications made in the dialog box and to close it. The analysis results are placed in the Reporting Documents workspace node after running (updating) the project. |
Denotes a split node
Denotes a terminal node
icon to produce a bar graph that pictorially shows the importance ranking on a 0-100 scale for each predictor variable considered in the analysis. This plot can be used for visual inspection of the relative importance of the predictor variables used in the analysis and, thus, helps to conclude which predictor variable is the most important predictor. See also, Predictor Importance in Statistica GC&RT, Interactive Trees, and Boosted Trees.