This node uses the C5.0 algorithm to build either a decision tree classifier or a rule set. A C5.0 model works by splitting the sample based on the field that provides the maximum information gain. Each subsample defined by the first split is then split again, usually based on a different field, and the process repeats until the subsamples cannot be split any further. Finally, the lowest-level splits are reexamined, and those that do not contribute significantly to the value of the model are removed or pruned.
- For more information about this node, see C5.0 Overview.
- For more information about the visualizations for this node, see C5.0 Visualizations.
Like your visualization? Why not deploy it? For more information, see Deploy a model.