Spam

Classify as Spam

Spam Handling

So the dictionary might be very, very large.

Classify as NOT Spam

Ham

Here are some examples of classification problems.

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

Most SPAM filters use human input.

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

How did we get this?

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

Spam

Who programmed the abbreviations? I did.

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

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

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

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.

Ham

This one is easy to calculate for SPAM and HAM.

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

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.

Show spam status in fancy headers

Now the tricky part comes up over here.

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.

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

Spam Status

But we had learned some other tricks along the way.

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 brand manager for that blue product spent 100 million dollars trying to interrupt me in one year.

Let's use the Laplacian smoother with K 1 to calculate the few interesting probabilities

And the correct answer is 23.

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

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

Let me start with a very simple message, and it contains a single word just to make it a little bit simpler.

Mark messages which have been classified as spam as read.

Again that's because your computer has neurons to distinguish spam from non spam email.

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

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

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

If you're not categorising people in the right way then you're going to start blasting people with things they don't want.

Let's do the same with 10 messages, and we get 6 spam.

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

For SPAM, it's 2 5, and the reason is, we had previously 3 out of 8 messages assigned to SPAM.