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Reduced Error Pruning And Rule Post Pruning

result in improved estimated accuracy Sort the pruned rules by their estimated accuracy and consider them in this sequence when classifying unseen instances. Patricia Riddle Fri May 15 13:00:36 NZST 1998

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Pruning Decision Tree In R

more about hiring developers or posting ads with us Stack Overflow Questions Jobs Documentation Tags Users decision tree pruning python Badges Ask Question x Dismiss Join the Stack Overflow Community Stack Overflow is a community of 6.3 million programmers, just like you, helping pessimistic pruning each other. Join them; it only takes a minute: Sign up Rule Post-pruning in Decision-Tree algorithm up vote 0 down vote favorite i read this post-pruning method and i want to know if iam getting this right by https://www.cs.auckland.ac.nz/~pat/706_98/ln/node92.html putting a simple example. The steps to prune the tree are: 1)Infer the decision tree from the training set, growing the tree until the training data fits as well as possible and allowing the overfitting to occur. 2)Convert the learned tree into an equivalent set of rules by creating one rule for each path from the root node to the leaf node. 3)Prune (generalize) each rule by removing any preconditions that result in improving its estimated http://stackoverflow.com/questions/33948029/rule-post-pruning-in-decision-tree-algorithm accuracy. 4)Sort the pruned rules by their estimated accuracy and consider them in this sequence when classifying subsequent instances So we have the dataset which is split into the training ,validation and test set. We use the training to grow the tree and as result we have the following rules: (Outlook = Sunny " Humidity = High) => N (Outlook = Sunny " Humidity = Low) =>P (Outlook = Overcast) =>P (Outlook = Rain " Wind = Weak) =>P (Outlook = Rain " Wind = Strong) =>N Next step is to use the validate set to start prune the preconditions so, lets say validation set is: Outlook| Temp| Humidity| Wind |Tennis Rain | Low | High | Weak | N Rain | High| High | Strong| N The one rule that result in improving its estimated accuracy is (Outlook = Rain " Wind = Strong) =>N, thats why we are going to prune the wind attribute. (im not sure about this step) New rules sorted from general to more specific: (Outlook = Sunny " Humidity = High) => N (Outlook = Sunny " Humidity = Low) =>P (Outlook = Overcast) =>P (Outlook = Rain ") =>N (Outlook = Rain " Wind = Weak) =>P Finally we use the test set to classify the examples with the new rules. Please if iam wrong somewhere let me know.Thank you algorithm

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