Spam

Ham

Spam Handling

Classify as Spam

So the dictionary might be very, very large.

Classify as NOT Spam

Spam

Who programmed the abbreviations? I did.

Ham

First I must solicit your confidence in this transaction, this is by virtue of its nature being utterly confidential and top secret...

99 MlLLlON EMAlL ADDRESSES FOR ONLY 99

How did we get this?

Y that we can think of as taking on two values, either zero or one, either a spam or not spam, fraudulent or not fraudulent, malignant or benign.

The assignment of the 2 classes, you know, spam, no spam, and so on the assignment of the 2 classes to positive and negative, to 0 and 1 is somewhat arbitrary and it doesn't really matter.

Had an old Dell Dimension XPS sitting in the corner and decided to put it to use. And so on and so on.

And so we would just look for links that had 1 comment by the same author and mark it as spammers.

Most SPAM filters use human input.

This one is easy to calculate for SPAM and HAM.

Here are some examples of classification problems.

It's my turn. Noodles and Spam, okay.

Spam Status

Show spam status in fancy headers

But we had learned some other tricks along the way.

Why? Because I got a lot of spam.

And we figured if a link was good on reddit, that link should have some authority.

Now the tricky part comes up over here.

I apologize to the other 3000 or so it was probably spam.

And users, legitimate users, rarely commented on their own link as the first comment.

This form is easily transformed into this expression over here, the probability of the message given spam times the prior probability of spam over the normalizer over here.

Sorry, no spam detection tools have been found. Install your spam detection software and re run this wizard.

It's 0 because the maximum of the estimate for the probability of today in spam is 0.

Yeah, I'm not sure if it's gonna be cool with them that I spam their emails.

Let's say k equals 1, and let's assume we get one message, and that message was spam, so we're going to write it one message, one spam.

And what we care about is what's our prior probability of spam that maximizes the likelihood of this data?

And the correct answer is 23.

If you have labeled data, you know, with spam and non spam e mail, we'd treat this as a Supervised Learning problem.

And just as before we count the word secret in SPAM and in HAM as I've underlined here.

It can't be that a single word determines the entire outcome of our analysis.

Mark messages which have been classified as spam as read.

And again, it might look like spam because the word secret occurs.

like anybody can submit anything, that it would just be quickly overrun with spam.

To see if we apply Bayes rule, which multiples the prior for spam ness with the conditional probability of each word given spam.

So you think on a site like reddit, where it's like the Wild West, there's no rules,

So in an email client like this you might click this spam button to report some email as spam, but not other emails and.

You can toss them easily into the naive base model and get better classification.