This paper proposes a simple decision tree pruning method based on RST (Rough Set Theory). Depth-fitting ratio is introduced for pruning a constructed decision ...
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Pruning decision trees is an effective way to overwhelm over-fitting in practice. Various pruning methods have been proposed in many literatures.
Various pruning methods have been proposed in many literatures. This paper gives a new decision tree pruning method based on Rough Set Theory (RST).
This paper proposes a simple decision tree pruning method based on. RST (Rough Set Theory). Depth-fitting ratio is introduced for pruning a constructed decision ...
Depth-fitting ratio is introduced for pruning a constructed decision tree, which involves both the depth and the explicit degrees of the sub-trees under ...
Nov 27, 2019 · Pruning is a technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that provide little ...
Jan 28, 2020 · I understand that pruning a decision tree will lead to a reduction in variance and also a reduction of the tree complexity, but how is the ...
Missing: RST | Show results with:RST
This paper proposes a simple decision tree pruning method based on RST (Rough Set Theory). Depth-fitting ratio is introduced for pruning a constructed decision ...
Apr 27, 2021 · I can't get my pruned tree to actually "prune". It just returns the exact same thing as the first decision tree. Partition data into 70% for training 30% for ...
Missing: RST | Show results with:RST
Apr 26, 2020 · The post pruning is taken place after the tree is already built, so it doesn't need any max_depth or smth like this because it will prune due to some criterion ...