Nmap Development mailing list archives
Prabhjyot's Status Report #10 of 17
From: Prabhjyot Singh Sodhi <prabhjyotsingh95 () gmail com>
Date: Tue, 5 Jul 2016 13:05:58 +0530
Hey list, This week I finally started working on the multi-stage random forest classifier. Here it goes, Accomplishments: - Use Precision and Recall in addition to accuracy. I managed to calculate the precision and recall for our test cases, but the results didn't seem to help with the comparison of models. For the uninitiated, Precision and recall in classifications context can be used to determine what kinds of prints are being predicted correctly. (For example we might want popular query prints say linux 4.0 to be predicted correctly compared to say a HP printer. (Accuracy considers them alike.) Having said that, the problem in our case is the low number of test prints. Most of my test cases deal with 20/40 prints (total: 301). This makes it difficult to compare precisions b/w groups since the number of test cases per group are even lower. - Started working on training the basic forests for the multi-stage classifier. This includes adding a new target field which identifies how are the prints classified in the first level. As of now we have started with 5 classes of prints for the first stage: Linux, Windows, Macintosh, BSD, Others - Apart from this I wrote some more documentation trying to explain the models. I will put these docs up soon for feedback. Priorities: - Get Prediction results from the multi-stage classifier - tune the multi-stage classifier - create a doc about the multi-stage classifier - Read about converting the Makefile generated by cmake to be used by autotools since opencv uses cmake while we'll need to integrate it with autotools in nmap Please pardon the delay in this week's report and feel free to write back with any suggestions/ questions Cheers, Prabhjyot
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- Prabhjyot's Status Report #10 of 17 Prabhjyot Singh Sodhi (Jul 05)