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

So the dictionary might be very, very large.

Spam

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.

Who programmed the abbreviations? I did.

Ham

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

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.

Spam Handling

Classify as Spam

Why? Because I got a lot of spam.

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

Classify as NOT Spam

Spam

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

Most SPAM filters use human input.

Mark messages which have been classified as spam as read.

Ham

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

But we had learned some other tricks along the way.

99 MlLLlON EMAlL ADDRESSES FOR ONLY 99

How did we get this?

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

The final quiz, let's assume our message is Today is secret.

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

This one is easy to calculate for SPAM and HAM.

Here are some examples of classification problems.

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.

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.

Spam Status

Show spam status in fancy headers

Allow out of office replies to be sent to messages marked as SPAM.

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

And I've had to do this on ASCII Chan and on my blog as well because a lot of you guys have submitted a bunch of crap to ASCII Chan and my blog that I didn't want to appear.

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

A lot of that stuff is just sitting in memcache and only you see it, but if you were to change computers, you would find that that submission disappears.

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

If there were 100 users all submitting crap but they had the same password, it was all spam, and we could just remove it.

In fact, these items that are sitting in the room change how much the subjects think they're going to enjoy the potato chips.

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

For example, for the prior probability, we found that 3 8 messages are spam.

What is p (spam) for the Laplace smoothing of k 1?

When you go through email you have a button called IS SPAM which allows you as a user to flag SPAM and occasionally you will say an email is SPAM.

Now the tricky part comes up over here.