Decision Tree Post-Pruning Algorithm
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A Decision Tree Post-Pruning Algorithm is a Decision Tree Pruning Algorithm (a model complexity and error reduction algorithm) that can solve a Decision Tree Post-Pruning Task.
- Context:
- It can range from being a Classification Tree Post-Pruning Algorithm to being a Ranking Tree Post-Pruning Algorithm to being a Regression Tree Post-Pruning Algorithm.
- See: Decision Tree Pree-Pruning Algorithm.
References
1998
- (Kononenko, 1998) ⇒ Igor Kononenko. (1998). “The Minimum Description Length Based Decision Tree Pruning.” In: Proceedings of the 5th Pacific Rim International Conference on Artificial Intelligence (PRICAI 1998).
- ABSTRACT: We describe the Minimum Description Length (MDL) based decision tree pruning. A subtree is considered unreliable and therefore is pruned if the description length of the classification of the corresponding subsets of training instances together with the description lengths of each path in the subtree is greater than the description length of the classification of the whole subset of training instances in the current node. We compare the performance of our simple, parameterless, and well-founded MDL method with some other methods on 18 datasets. The classification accuracy using the MDL pruning is comparable to other approaches and the decision trees are nearly optimally pruned which makes our method an attractive tool for obtaining a first approximation of the target decision tree during the knowledge discovery process.