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Rule Post-Pruning
- Applied in C4.5.
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Steps ( [1])
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Infer the decision tree from the training set, growing the set until the training data is fit as well as possible and allowing overfitting to occur.
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Convert the learned tree into an equivalent set of rules by creating one rule for each path from the root node to a leaf node.
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Prune (generalize) each rule by removing any preconditions that result in improving its estimated accuracy.
- Sort the pruned rules by their estimated accuracy, and consider them in this sequence when classifying subsequent instances.
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Rule accuracy estimation based on the training set using a pessimistic estimate: C4.5 calculates standard deviation and takes the lower bound as estimate for rule performance.