I would like to get the information about the algorithm/performance of Random Forest node available under
Analytics>Mining>Decisio Tree Ensemble>Random Forest>Classification> Random Forest Learner
and
Analytics>Weka>Classification Algorithms> trees>Random Forest
Are both same?
Are their performance same on same dataset?
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Arvind
Hello Arvind,
they are definitely not the same. The Weka Random Forest is part of the Weka integration in the KNIME AP.
As far as I can tell, the Weka Random Forest implementation closely follows Leo Breiman's original Random Forest implementation very closely. In our own implementation we use a number of modifications that have been proposed in recent years. Most of those affect the tree building, which of course has a lot of impact on the whole forest. An example of such a modification is the calculation of binary nominal splits, for which we use techniques that allow a more efficient calculation.
When it comes to performance I believe it is save to say that the KNIME AP Random Forest is in most cases faster (see this e.g. this thread https://tech.knime.org/forum/knime-general/differences-between-weka-random-forest-and-knime-3s-random-forest-learner) and can at least compete in terms of accuracy/roc (or what kind of metric You are using).
However, Your results will always depend on Your data and trying out both nodes might be worth a shot and is super easy to accomplish in the KNIME AP ;-)
If You would like more details on the implementation feel free to ask.
Cheers,
nemad