What algorithm does Decision Tree used in Rapidminer?

New Altair Community Member
Updated by Jocelyn
Hi all,
What kind of Decision tree algorithm does Rapidminer used? Does it take care of imbalanced data?
Thanks!
Johnny
What kind of Decision tree algorithm does Rapidminer used? Does it take care of imbalanced data?
Thanks!
Johnny
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Well, as I said, it's similar to C4.5. In each node the split attribute is chosen by iterating all attributes, finding the best split for each attribute with respect to the splitting criterion, and then using the attribute that maximizes the chosen criterion.
For nominal attributes always one branch for each value is created. For numerical/date attributes always a binary split is performed. To find the best split value all possible values in the training data are tried.
The procedure is repeated until you have pure leaves or one of the pre-pruning conditions is met. Then optionally some post-pruning is applied.
Best regards,
Marius
For nominal attributes always one branch for each value is created. For numerical/date attributes always a binary split is performed. To find the best split value all possible values in the training data are tried.
The procedure is repeated until you have pure leaves or one of the pre-pruning conditions is met. Then optionally some post-pruning is applied.
Best regards,
Marius
In general, exploring your data with a decision tree is a good idea, applying the model
on unseen data not always.
You may preprocess your data by applying the operator "Sample (Bootstrapping)"
but you should switch off preprocessing in the testing step.
For further documentation please refer to the documentation of the decision tree operator.