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## One of the questions that arises in a decision tree algorithm is the optimal size.

tree1 = prune(tree) returns the decision tree tree1 that is the full, unpruned tree, but with optimal pruning information added. This is useful only if you created tree by pruning another tree, or by using fitrtree with pruning set 'off'. If you plan to prune a tree multiple times along the optimal pruning sequence, it is more efficient to create the optimal pruning sequence first. tree1 = prune(tree) returns the decision tree tree1 that is the full, unpruned tree, but with optimal pruning information added.

This is useful only if you created tree by pruning another tree, or by using the fitctree function with pruning set 'off'. If you plan to prune a tree multiple times along the optimal pruning sequence, it is more efficient to create the optimal pruning sequence first. The first decision is whether x1 is smaller than If so, follow the left branch, and see that the tree classifies the data as type 0. If, however, x1 exceedsthen follow the right branch to the lower-right triangle node.

Here the tree asks if x2 is smaller than prune_tree = prune (tree, 'Level', max(bushmulching.barist)-1); % prune tree to have decision stump. % if the prune tree still has more than one decision south brunswick tree removal (three inner nodes) use the max (bushmulching.barist) to reduce it to just one node. if length(prune_bushmulching.barze) > 3.

prune_tree = prune (tree, 'Level', max(bushmulching.barist)). To prune a tree, the tree must contain a pruning sequence. By default, both fitctree and fitrtree calculate a pruning sequence for a tree during construction. If you construct a tree with the 'Prune' name-value pair set to 'off', or if you prune a tree to a smaller level, the tree does not contain the full pruning.

Error-Based Pruning. To prune the given decision tree using the error-based pruning algorithm (outlined in C Programs for Machine Learning), call (in the MATLAB environment): T = prune_tree_C45(T,A,B,certainty_factor) where: T: matrix representing the decision tree in the MATLAB environment. A, B: MATLAB representation of matrices A and B.

Dec 24, prune: enable/disable reduced-error pruning; minparent/minleaf: allows to specify min number of instances in a node if it is to be further split; nvartosample: used in random trees (consider K randomly chosen attributes at each node) weights: specify weighted instances; cost: specify cost matrix (penalty of the various errors).

PruneAlpha(1) is for pruning level 0 (no pruning), PruneAlpha(2) is for pruning level 1, and so on. PruneList An n -element numeric vector with the pruning levels in each node of tree, where n is the number of nodes. If Prune is 'on', then the software trains the classification tree learners without pruning them, but estimates the optimal sequence of pruned subtrees for each learner in the ensemble or decision tree binary learner in ECOC models.

Otherwise, the software trains the classification tree learners without estimating the optimal sequence of pruned subtrees.