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At first glance, this might appear to be a "Hello-World" machine learning problem straight out of a textbook or tutorial: we simply train a Naive Bayes on a set of bad ads versus a set of good ones. However this is apparently far from being the case - while Google is understandably shy about hard numbers, the paper mentions several issues which make this especially challenging and notes that this is a business-critical problem for Google.
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There are many useful suggestions in this post.
At first glance, this might appear to be a "Hello-World" machine learning problem straight out of a textbook or tutorial: we simply train a Naive Bayes on a set of bad ads versus a set of good ones. However this is apparently far from being the case - while Google is understandably shy about hard numbers, the paper mentions several issues which make this especially challenging and notes that this is a business-critical problem for Google.
--
There are many useful suggestions in this post.
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