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From Ranjitha Chandrashekar <Ranjitha...@hcl.com>
Subject Partial Implementation
Date Thu, 17 Jan 2013 08:58:31 GMT
Hi

I am using Partial Implementation for Random Forest classification.

I have a training dataset with labels class0, class 1, class 2.  The decision forest is built
on this training dataset.  The classification for the test dataset is computed using the same
data descriptor generated for the training dataset.  I am able to generate confusion matrix,
accuracy details with the test data set with class variable.

However I also need to make a classification for a scenario, where test data may not have
the class variable or class values are not known.  For ex, assume test data is about future
data points, for which class values will have to be computed only in the future.


*         How is it possible to classify the test data set, where the class label is not defined
or not known. I have tried using default labels like "unknown", "NO_LABEL". It doesnt seem
to work.


*         How to set the class label as "unknown" in the testing dataset.

Looking forward to your reply,

Thanks
Ranjitha.




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