A visualization of classification and regression trees.
Classification or regression tree.
Data from a selected tree node.
Data set with an additional attribute for selection labels.
This is a versatile widget with 2-D visualization of a classification tree. The user can select a node, instructing the widget to output the data associated with the node, thus enabling explorative data analysis.
- Information on the input.
- Display options:
- Zoom in and zoom out
- Select the tree width. The nodes display information bubbles when hovering over them.
- Select the depth of your tree.
- Select edge width. The edges between the nodes in the tree graph are drawn based on the selected edge width.
- All the edges will be of equal width if Fixed is chosen.
- When Relative to root is selected, the width of the edge will correspond to the proportion of instances in the corresponding node with respect to all the instances in the training data. Under this selection, the edge will get thinner and thinner when traversing toward the bottom of the tree.
- Relative to parent makes the edge width correspond to the proportion of instances in the nodes with respect to the instances in their parent node.
- Define the target class, which you can change based on classes in the data.
- Press Save image to save the created classification tree graph to your computer as a .svg or .png file.
- Produce a report.
Below, is a simple schema, where we have read the data, constructed the classification tree and viewed it in our tree viewer. If both the viewer and Classification Tree are open, any re-run of the tree induction algorithm will immediately affect the visualization. You can thus use this combination to explore how the parameters of the induction algorithm influence the structure of the resulting tree.
Clicking on any node will output the related data instances. This is explored in the schema below that shows the subset in the data table and in the Scatterplot. Make sure that the tree data is passed as a data subset; this can be done by connecting the Scatterplot to the File widget first, and connecting it to the Classification Tree Viewer widget next. Selected data will be displayed as bold dots.
Tree Viewer can also export labelled data. Connect Data Table to Tree Viewer and set the link between widgets to Data instead of Selected Data. This will send the entire data to Data Table with an additional meta column labelling selected data instances (Yes for selected and No for the remaining).
Finally, Tree Viewer can be used also for visualizing regression trees. Connect Regression Tree to File widget using housing.tab data set. Then connect Tree Viewer to Regression Tree. The widget will display the constructed tree. For visualizing larger trees, especially for regression, Pythagorean Tree could be a better option.