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 antispam Lessons we learned while protecting Gmail users [VID]

Adjust detection to product use-cases – Tune your machine-learning classifiers to match your product needs When you make a statistical decision, you can err on one side of the decision or the other. For example, you can decide to detect more spam at the expense of flagging good mail as spam (false positives) or you can reduce the number of good emails flagged as spam at the expense of having more spam not detected (false negatives). How to balance the two (to a reasonable level, that is) is product [...] [more]
elie.net    Spam, Study, Deliverability
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