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Pruning is a data compression technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that are non-critical and redundant to classify instances.
A decision tree is a decision support recursive partitioning structure that uses a tree-like model of decisions ... Design rationale – Explicit listing of design ...
Once the decision tree is constructed, then the new branches that can be added productively to the tree are identified. Then they are grafted to the existing tree to improve the decision making process. Pruning and Grafting are complementary methods to improve the decision tree in supporting the decision.
A decision tree or a classification tree is a tree in which each internal (non-leaf) node is labeled with an input feature. The arcs coming from a node labeled with an input feature are labeled with each of the possible values of the target feature or the arc leads to a subordinate decision node on a different input feature.
Alpha–beta pruning is a search algorithm that seeks to decrease the number of nodes that are evaluated by the minimax algorithm in its search tree. It is an adversarial search algorithm used commonly for machine playing of two-player combinatorial games (Tic-tac-toe, Chess, Connect 4, etc.). It stops evaluating a move when at least one ...
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Prune and search is a method of solving optimization problems suggested by Nimrod Megiddo in 1983. [ 1 ] The basic idea of the method is a recursive procedure in which at each step the input size is reduced ("pruned") by a constant factor 0 < p < 1 .
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