Pythagorean forest for visualising random forests.
Classification / regression tree models as random forest.
A selected classification / regression tree model.
Pythagorean Forest shows all learned classification tree models from Random Forest widget. It displays then as Pythagorean trees, each visualization pertaining to one randomly constructed tree. In the visualization, you can select a tree and display it in Pythagorean Tree wigdet. The best tree is the one with the shortest and most strongly colored branches. This means few attributes split the branches well.
Widget displays both classification and regression results. Classification requires discrete target variable in the data set and a Classification Tree widget on the input, while regression requires a continuous target variable with Regression Tree on the input.
Information on the input random forest model.
- Depth: set the depth to which the trees are grown.
- Target class: set the target class for coloring the trees. If None is selected, tree will be white. If the input is a classification tree, you can color nodes by their respective class. If the input is a regression tree, the options are Class mean, which will color tree nodes by the class mean value and Standard deviation, which will color then by the standard deviation value of the node.
- Size: set the size of the nodes. Normal will keep nodes the size of the subset in the node. Square root and Logarithmic are the respective transformations of the node size.
- Zoom: allows you to se the size of the tree visualizations.
Save Image: save the visualization to your computer as a .svg or .png file. Report: produce a report.
Pythagorean Forest is great for visualizing several built trees at once. In the example below, we’ve plotted all 10 trees we’ve grown with Random Forest Regression. When changing the parameters in Random Forest Regression, visualization in Pythagorean Forest will change as well.
Then we’ve selected a tree in the visualization and inspected it further with Pythagorean Tree widget.
Beck, F., Burch, M., Munz, T., Di Silvestro, L. and Weiskopf, D. (2014). Generalized Pythagoras Trees for Visualizing Hierarchies. In IVAPP ‘14 Proceedings of the 5th International Conference on Information Visualization Theory and Applications, 17-28.