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Prune tree

treeprune will be removed in a future release. Use fitctree or fitrtree to grow a tree. Then use prune (ClassificationTree) or prune (RegressionTree) instead of treeprune.


t2 = treeprune(t1,'level',level)
t2 = treeprune(t1,'nodes',nodes)
t2 = treeprune(t1)


t2 = treeprune(t1,'level',level) takes a decision tree t1 as created by the treefit function, and a pruning level, and returns the decision tree t2 pruned to that level. Setting level to 0 means no pruning. Trees are pruned based on an optimal pruning scheme that first prunes branches giving less improvement in error cost.

t2 = treeprune(t1,'nodes',nodes) prunes the nodes listed in the nodes vector from the tree. Any t1 branch nodes listed in nodes become leaf nodes in t2, unless their parent nodes are also pruned. The treedisp function can display the node numbers for any node you select.

t2 = treeprune(t1) returns the decision tree t2 that is the same as t1, but with the optimal pruning information added. This is useful only if you created t1 by pruning another tree, or by using the treefit function with pruning set 'off'. If you plan to prune a tree multiple times, it is more efficient to create the optimal pruning sequence first.

Pruning is the process of reducing a tree by turning some branch nodes into leaf nodes, and removing the leaf nodes under the original branch.


Display the full tree for Fisher's iris data, as well as the next largest tree from the optimal pruning sequence:

  load fisheriris;
  t1 = treefit(meas,species,'splitmin',5);
  treedisp(t1,'names',{'SL' 'SW' 'PL' 'PW'});

  t2 = treeprune(t1,'level',1);
  treedisp(t2,'names',{'SL' 'SW' 'PL' 'PW'});


[1] Breiman, L., J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Boca Raton, FL: CRC Press, 1984.

Introduced before R2006a

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