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This pruning procedure, guided by a minimal cost complexity measure, creates a nested subset of trees.
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After a decision tree is fully grown, CART analysis conducts a minimal cost-complexity pruning method to avoid overfitting.
In order to control for overfitting and finding the optimal CART based tree size, minimal cost-complexity pruning was applied.
As an attempt to reduce the tree size, the optimal cut subtree could be detected using minimal cost-complexity pruning based on cross validation (see [ 26]).
The model-building set was then used to establish a tree that was pruned by use of the validation set to achieve an estimation of the most appropriate tree through minimal cost-complexity pruning [ 20, 21].
This leads to the conclusion that the worst-case ε-complexity of the considered problem in Fr,ϱ is Θ(ε−1/(r+ϱ)), and the minimal cost is achieved by the defined algorithm that only needs function evaluations.
"These resolutions help contain those risks at minimal cost.
They also vary in cost, complexity and intrusiveness.
This cost complexity of O(K×Nsense).
The structure was designed for both minimal cost and minimal weight.
Then, what strategies are implemented to optimize the cost function at the minimal cost?
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