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Home Support FAQs CART How can I control splitting interactively?

How can I control splitting interactively? Featured

CART's tree-building process can be interactively controlled by using the INTERACTIVE option on the ERROR command:

ERROR EXPLORATORY / INTERACTIVE

When building is started, CART will produce and display information on the first node, including lists of Competitor and Surrogate splits. You will be prompted with:

>

The commands that can be used during interactive splitting are:

  • REPLACE COMPETITOR=n: Choose to split on competitor n from the list.
  • REPLACE SURROGATE=n: Choose to split on surrogate n from the list.
  • REPLACE LINEAR=n: Choose to split on linear combination n from the list.
  • REPLACE SPLIT var=n: Choose to split on a specific variable var at split value n.
  • REPLACE SPLIT var=n1,n2,n3,… : Choose to split on a specific categorical variable var with split levels n1, n2, n3,&hellip
  • NEXT: move on to the next node.
  • CONTINUE: stop interactive splitting and let the rest of the tree grow automatically.
  • QUIT: Totally stop altogether.
  • FRESH: Redo the current node unconstrained, as it was when you first saw it.
  • ABOVE DEPTH = n: Allows interactive splitting only above a given depth.
  • RESAMPLE: If the node is subsampled, this will choose a new subsample and generate new splits.

Several important caveats about interactive splitting:
  • You must be in command mode to use this feature. Future versions of CART will enable this feature via the GUI.
  • Because the interactively split tree is an exploratory tree, it will not be pruned back. To avoid growing a tree that is too large, be sure to limit the size of the tree by setting the complexity, depth, or number of nodes prior to building the tree.
  • If you only want to interactively split the top three nodes, use ABOVE DEPTH=2 to avoid having to interactively split other nodes on the left side of the tree before returning to the second node on the right side of the tree.

Steinberg, Dan and Phillip Colla. CART—Classification and Regression Trees. San Diego, CA: Salford Systems, 1997.