<transcript><audio_77>to the other approaches here's the simplified Venn diagram where I showed the classifier no Mightiest and the lists combined as expected the classifier no matias are largely in agreement with each other because that's what the classifier has been trained on</transcript><transcript><audio_78>the classifier is slightly more aggressive however it labels close to 14,000 samples as positive and some of these suppose with false positives that the classifier finds are actually an agreement with the filter lists so it</transcript><transcript><audio_79>means it's able to overcome the false negatives in the no more 80s labeling system there are of course also some false negatives and most of these we hope to fix by better data balancing also improvements within no more ATS</transcript><transcript><audio_80>itself to conclude I presented nom ideas which is a system for automatically labeling which packets are sent by a and T libraries the data collected with normal ATS can be used to train classifiers and evaluation with popular</transcript> to the other approaches here's the simplified Venn diagram where I showed the classifier no Mightiest and the lists combined as expected the classifier no matias are largely in agreement with each other because that's what the classifier has been trained on the classifier is slightly more aggressive however it labels close to 14,000 samples as positive and some of these suppose with false positives that the classifier finds are actually an agreement with the filter lists so it means it's able to overcome the false negatives in the no more 80s labeling system there are of course also some false negatives and most of these we hope to fix by better data balancing also improvements within no more ATS itself to conclude I presented nom ideas which is a system for automatically labeling which packets are sent by a and T libraries the data collected with normal ATS can be used to train classifiers and evaluation with popular
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