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Decision Tree Training Set Error

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Decision Tree Training Data

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Classification Error Decision Tree

can ask a question Anybody can answer The best answers are voted up and rise to the top Maximum training error for a decision tree up vote 2 down vote favorite For any dataset $D$, with the label space containing m labels. Using decision tree, can we calculate the maximum training error on that? Yes I have to admit, it's a homework question and I have completely

Error Rate Decision Tree

no clues on that. Could any one gives me some hint on that? And what is label, does it mean edges of the tree? machine-learning self-study classification share|improve this question edited Oct 1 '15 at 4:48 asked Oct 1 '15 at 4:31 xxx222 1478 Hi Xupeng, welcome to Cross-validated. It is a good question. However, if it is a homework question, please add the tag "homework". –Simone Oct 1 '15 at 4:42 Also you can add latex notations if you use the \$ symbol. E.g. for any dataset \$D\$.. –Simone Oct 1 '15 at 4:43 Thanks, I have updated my question –xxx222 Oct 1 '15 at 4:52 add a comment| 1 Answer 1 active oldest votes up vote 1 down vote accepted A decision tree is a classification model. You can train a decision tree on a training set $D$ in order to predict the labels of records in a test set. $m$ is the possible number of labels. E.g. $m = 2$ you have a binary class problem, for example classifying patients who might either have or not have a disease; $m > 2$ you have multi-class problem, for

 

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decision tree training error

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misclassification error tree

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