Hi Ranjitha, just put any numerical value in the label attribute. You should be able to classify the data, but you won't be able to compute the confusion matrix or the accuracy.
On Thu, Jan 17, 2013 at 12:15 PM, Ranjitha Chandrashekar < ranjitha...@hcl.com> wrote: > 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. > > > > ::DISCLAIMER:: > > ---------------------------------------------------------------------------------------------------------------------------------------------------- > > The contents of this e-mail and any attachment(s) are confidential and > intended for the named recipient(s) only. > E-mail transmission is not guaranteed to be secure or error-free as > information could be intercepted, corrupted, > lost, destroyed, arrive late or incomplete, or may contain viruses in > transmission. The e mail and its contents > (with or without referred errors) shall therefore not attach any liability > on the originator or HCL or its affiliates. > Views or opinions, if any, presented in this email are solely those of the > author and may not necessarily reflect the > views or opinions of HCL or its affiliates. Any form of reproduction, > dissemination, copying, disclosure, modification, > distribution and / or publication of this message without the prior > written consent of authorized representative of > HCL is strictly prohibited. If you have received this email in error > please delete it and notify the sender immediately. > Before opening any email and/or attachments, please check them for viruses > and other defects. > > > ---------------------------------------------------------------------------------------------------------------------------------------------------- >
