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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 no clues on that. Could any one gives me some hint on that? And what is label, does it mean edges of the tree?

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  • $\begingroup$ Hi Xupeng, welcome to Cross-validated. It is a good question. However, if it is a homework question, please add the tag "homework". $\endgroup$ – Simone Oct 1 '15 at 4:42
  • $\begingroup$ Also you can add latex notations if you use the \$ symbol. E.g. for any dataset \$D\$.. $\endgroup$ – Simone Oct 1 '15 at 4:43
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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 example if you had to classify news in "politics", "sport", and "culture".

Think about how decision trees work. I wrote an explanation here. The hint I can give to you is that in your homework attributes are not relevant to the class. Also, think about the way decision trees classify records on leaf nodes.

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  • $\begingroup$ So does it mean the dataset that may meet the maximum training error here has to be the worst one (one with the highest entropy) ? $\endgroup$ – xxx222 Oct 1 '15 at 5:26
  • $\begingroup$ Yes, I think you have to think about the worst dataset where your decision tree is just a root node. $\endgroup$ – Simone Oct 1 '15 at 7:26
  • $\begingroup$ Do you mean considering one root node situation is good enough? I am confused because of I know nothing about the information of node, which centers in the decision tree theory. $\endgroup$ – xxx222 Oct 1 '15 at 7:36
  • $\begingroup$ I mean, what is the worst case for a decision tree? that attributes are totally unpredictive to the class. In this case you wouldn't even grow a decision tree. You would just have a single root node, which can also be seen as a single leaf node. Now you have just to think about how you classify records on a leaf node: majority voting. $\endgroup$ – Simone Oct 1 '15 at 7:39
  • $\begingroup$ Does it mean in the worst case we can't even select a proper feature to grow the tree? So what should I do now? Randomly allocate m classifications? What does the majority means here? $\endgroup$ – xxx222 Oct 1 '15 at 7:56

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