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Spam Classification Example
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Seem a little more complicated because touch high abstraction difference in the field machine learning
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more complex problems for machine learning, math formula in this video tends to help much
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this lesson is about how we prioritising in the machine learning problem
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How do we use supervised learning to classify spam or not-spam?
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Non spam on the left denotes by using number in words
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Features:Choose words related to purchasing to spam, and subject name to non-spam.
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This is spam/non-spam classification with logistic regression method.
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We create 100 words feature vector.
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List whether or not the each word is in the list are appear on the examples. If it does, mark 1, otherwise 0
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Rather than manually choosing list of words, we can instead choosing the most frequent words (10K-50K) by training examples
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Spend time to have high accuracy
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Honeypot:Â create lots of fake email, and let it be spammed. So we can have those fake emails as our training examples.
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Gather data by email header, which the content has routing information. They sometimes take an unusual routes of source.
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in Email body,misspellings in spam is often intended to avoid spam filtering words.
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These are options on what to do to in spam classifier examples.
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Often many machine learning scientist spend some time and fixated about these options. Sometimes this doesn't fruitful at all.
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What's not recommended is when people use "gut feeling" like the solution they feel right when they woke up in the morning.
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Next error analysis and how we spend time choosing the right way to increase the learning performance
