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No Tree


The most frustrating outcome is to learn that CART has apparently not grown a tree. In this circumstance you will obtain a Tree Sequence Summary, but no node detail and no analytical results beyond a variable importance ranking.


SOLUTION 1: PICK a Tree

While you are still in BUILD, issue the PICK command to generate a tree from the tree sequence. Try something like:

PICK NODES = 4

which produces an exploratory tree including all the standard output on node detail, learning sample cost statistics, and so on. Be aware, however, that CART has determined that no tree should be generated, and therefore that the predictive performance of your tree is likely to be poor.

If you have already left BUILD and are back at the CART phase of analysis, grow an exploratory tree with:

ERROR EXPLORE

setting the complexity to a value that limits the tree to a size you wish to examine. Find the complexity value you need from the previous tree sequence output.


SOLUTION 2: Change Priors

CART can experience problems when some levels of a target variable have very few cases and you have specified PRIORS DATA (or PRIORS LEARN or PRIORS TEST). When the costs of misclassification are strictly proportional to the relative frequency of a class in the learning sample, CART may find that it is more accurate not to grow a tree. For example, in a binary problem with 95% class 1 and 5% class 2 cases, classifying all cases as class 1 from the start may yield the lowest costs on test data. To deal with this situation, try PRIORS EQUAL or PRIORS MIX, even if the classes have not been over- or under-sampled. The new priors will alter the penalty for misclassifying the rarer cases and may yield a tree.


SOLUTION 3: Change Costs

This solution is similar to changing priors. By changing costs you penalize CART for failing to split the root node, which might be enough to generate a test-validated tree.


SOLUTION 4: Change the Splitting Rule

For classification trees, Gini is the default node-splitting method; for regression trees, it is LS. You can sometimes induce a reluctant CART to generate test-validated trees by trying another method, for example, twoing or ordered twoing. One method that often proves useful is to adjust CART towards equal-sized child nodes with the POWER option. Thus,

METHOD TWOING, POWER = 1

sets the center-cutting exponent to one, decreasing the chance of extremely unbalanced child nodes (end cuts).


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