The Altair Community is migrating to a new platform to provide a better experience for you. In preparation for the migration, the Altair Community is on read-only mode from October 28 - November 6, 2024. Technical support via cases will continue to work as is. For any urgent requests from Students/Faculty members, please submit the form linked here
"How to interpret One-class SVM output?"
mohammadreza
Member Posts: 23 
Contributor II
Hi gurus,
I have used one-class SVM for binary classification in RapidMiner. I only trained the model with false examples. And the output is "inside" either "outside". Now in order to calculate precision and recall, I need true positive, false positive and so on. But due to the fact that I trained the model only with false examples, I am not sure if the following is right:
"inside = non-anomaly=false"
"outside = anomaly=true"
Thanks in advance for any idea which may help me come out of this confusion.
I have used one-class SVM for binary classification in RapidMiner. I only trained the model with false examples. And the output is "inside" either "outside". Now in order to calculate precision and recall, I need true positive, false positive and so on. But due to the fact that I trained the model only with false examples, I am not sure if the following is right:
"inside = non-anomaly=false"
"outside = anomaly=true"
Thanks in advance for any idea which may help me come out of this confusion.
Tagged:
0
Answers
It was not a good idea for my problem to train the classifier with negative samples. I trained it with anomalies and the precision and recall have their original meaning now.