Hi,
I would like to ask about the selection of subtree for Decision tree.
What are the criterion to select in pair for Method option (Assessment, Largest and N) and Assessment measure option (Decision, Classifications, Average square error and Lift)?
How about in the Iteration plot where there is also similar selection (Average square tree, Miclassification tree, Sum of square error, maximum absolute error, and subtree assessment plot) to decide the optimal number of leaves?
I find it quite confusing and wish someone can explain this to me. Thank you.
Regards,
Potiu
Here is information about those properties. If you choose Assessment for the subtree method, then you should be able to see in the Iteration plot that the subtree selected has the best value for whatever assessment measure you chose, but you can view the other measures as well (in case you want to re-run using one of the other measures to get a different subtree).
Here is information about those properties. If you choose Assessment for the subtree method, then you should be able to see in the Iteration plot that the subtree selected has the best value for whatever assessment measure you chose, but you can view the other measures as well (in case you want to re-run using one of the other measures to get a different subtree).
I could understand in terms of Decision tree where we can select a sub tree.
In Gradient boosting as well I see subtree option:
As we have defined max depth(for example =2) already, In one iteration tree can maximum have 4 leaves(given max branch =2).
As the gradient boosting algorithm is a sequential...will it select the subtree before moving to the next iteration?
Please provide explanation in terms of gradient boosting. Thanks in advance
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