A pruning of a tree T with respect to an edge (v → u) is the removal from T of all nodes in T u v, except for v. Observe that every nonempty subtree of T is obtained by pruning T with respect to zero or more edges.
Thus, after this first tree was constructed, we started the evaluation and pruning of the tree using a second set of 8,000 randomly chosen genes.
Unlike REPTree classifier, this algorithm performs no pruning of the tree.
Growing, stopping, and pruning of the tree were determined by Gini improvement measures [ 20, 21].
This predictive resolution is a result of the trade-off between robustness, parsimony, and accuracy, reflected by the degree of pruning of the tree.
The elimination of non-significant variables in regression analysis based on backward variable selection is similar to the pruning of the tree that contains the maximum number of terminal nodes.
It uses a slash and mulch technique, with no burning, and the yearly thinning and pruning of the trees and vegetation to ensure adequate light for the crops.
Because decision tree algorithms commonly conduct pruning of the trees to remove unnecessary features, the derived rules generally involve only a small number of features.
Pruning of the trees (to correct for overtraining) was undertaken using the 1 SE rule described by [ 15] in combination with a maximal tree-depth of 4 layers.
The second rule R inout performs a 1-look-ahead in the search procedure based on the nodes' cardinality (Card) and allows an early pruning of the search tree.
