Good day Scikit-Learn Masters, I have used Scikit-Learns OCSVM module previously with satisfying results. However on my current tasks I have this problem for one-class analysis:
In my previous cases, I used OCSVM for Anomaly detector, and the normal classes in each cases were coming from one scenario. Now, I want to create one Anomaly detector system, with multiple normal scenario (in this case, 3 different normal scenario). Lets say I have scenario A, B and C, and I want to distinguish all data that is not coming from A and B and C. What I have been tried is combining all training data A and B and C into one data set and fit it using OCSVM module. When I tested the output model to several anomaly data-set it worked good. However, when I tested it against either one of the normal scenario, it gave a very high False Positives (AUROC: 99%). So my question, is it because a bad approach? by combining all the different normal data set into one training data set. Or is it because I was using it (the OCSVM) wrong? (i use 'rbf' kernel with nu and gamma set to 0.001) Or is it the case with wrong tools? another algorithm perhaps? I dont know if this is a proper question to ask here, so if it is not (maybe because this is just a Machine Learning question in general), just disregard it. Thank you in advance Best regards, Ady _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
