Naive Bayes classifiers work by correlating the use of tokens (typically words, or sometimes other things), with spam and non-spam e-mails and then using Bayes' theorem to calculate a probability that an email is or is not spam.
Naive Bayes spam filtering is a baseline technique for dealing with spam that can tailor itself to the email needs of individual users and give low false positive spam detection rates that are generally acceptable to users. It is one of the oldest ways of doing spam filtering, with roots in the 1990s.
History
The first known mail-filtering program to use a naive Bayes classifier was Jason Rennie's ifile program, released in 1996. The program was used to sort mail into folders. The first scholarly publication on Bayesian spam filtering was by Sahami et al. in 1998. That work was soon thereafter deployed in commercial spam filters. However, in 2002 Paul Graham greatly decreased the false positive rate, so that it could be used on its own as a single spam filter.
Various anti-spam techniques are used to prevent email spam (unsolicited bulk email).
No technique is a complete solution to the spam problem, and each has trade-offs between incorrectly rejecting legitimate email (false positives) vs. not rejecting all spam (false negatives) - and the associated costs in time and effort.
Anti-spam techniques can be broken into four broad categories: those that require actions by individuals, those that can be automated by email administrators, those that can be automated by email senders and those employed by researchers and law enforcement officials.
Detecting spam
Checking words: false positives
People tend to be much less bothered by spam slipping through filters into their mail box (false negatives), than having desired email ("ham") blocked (false positives). Trying to balance false negatives (missed spams) vs false positives (rejecting good email) is critical for a successful anti-spam system. Some systems let individual users have some control over this balance by setting "spam score" limits, etc. Most techniques have both kinds of serious errors, to varying degrees. So, for example, anti-spam systems may use techniques that have a high false negative rate (miss a lot of spam), in order to reduce the number of false positives (rejecting good email).
[http://bit.ly/N-Bayes] How can we distinguish spam from non-spam with a Naive Bayes classifier? We estimate the priors and multiple Bernoulli distributions for each class. Also learn how Naive Bayes can misclassify its own training examples.
published: 15 Jan 2014
Bayes Rule Example of Spam Filter
published: 17 Jul 2018
Bayes Rule example of spam filter
published: 15 Jul 2018
Find out about bayesian spam filter
Check out this great site on http://www.allspammedup.com/anti-spam/bayesian-spam-filtering/
How much of time and effort we end up wasting trying to sift through the mounds of spam that hits our mail boxes in order to get to the real mails that matter. With the help of Bayesian spam filter this can become a thing of the past. This is one of the most innovative and advanced of techniques that can help eliminate all sorts of spam. It was first heard of in the year 1998 but at that point of time no one actually paid much heed to it. Then, in 2002 people started to use these techniques to block spam from their inbox. Go on and allow Bayesian spam filter to help you wage a war against spam. Spam can be one of the worst things to our time management. It can eat into your time like nothing else ...
published: 02 Aug 2010
Spam Filtering Using Bayesian Algorithm
A presentation of how to use Bayes Rules in Business Problem
published: 16 Jun 2016
Naive Bayes, Clearly Explained!!!
When most people want to learn about Naive Bayes, they want to learn about the Multinomial Naive Bayes Classifier - which sounds really fancy, but is actually quite simple. This video walks you through it one step at a time and by the end, you'll no longer be naive about Naive Bayes!!!
Get the StatQuest Study Guide here: https://statquest.org/statquest-store/
For a complete index of all the StatQuest videos, check out:
https://statquest.org/video-index/
If you'd like to support StatQuest, please consider...
Buying my book, The StatQuest Illustrated Guide to Machine Learning:
PDF - https://statquest.gumroad.com/l/wvtmc
Paperback - https://www.amazon.com/dp/B09ZCKR4H6
Kindle eBook - https://www.amazon.com/dp/B09ZG79HXC
Patreon: https://www.patreon.com/statquest
...or...
YouTube Membersh...
published: 03 Jun 2020
Bayesian - Spam Filtering using C++
This video were created by Amadeuz Ezrafel and Gagas Wicaksono S1 PTI Offering D '12, State University of Malang, to fulfill Final Project of Discrete Mathematic lesson.
Many lack in this video, so please support us by giving like and comment. :)
If you want the complete coding in C++ please mail me: [email protected] ,
subject: Bayesian - Spam Filtering using C++
published: 13 May 2013
Bayes' Theorem EXPLAINED with Examples
Learn how to solve any Bayes' Theorem problem. This tutorial first explains the concept behind Bayes' Theorem, where the equation comes from, and finally how to use the formula in an example. Bayes' Theorem is one of the most common equations covered in Statistics due to its numerous applications to the real world. It is also one of the most misunderstood theorems, but this video will help clear all of that up!
For more help, visit: https://theacetutors.com
For more videos/content, visit: https://theacetutors.com/blog
Sign up for tutoring: https://theacetutors.com/register
published: 04 Apr 2023
Bayesian Spam Filter - C# Machine Learning - Development Timelapse and Math
Check out Moneylender Professional (loan servicing software) - https://moneylenderprofessional.com
I have to manually filter over 500 spam emails every day. Constantly clearing out the accreting digital refuse is hugely time consuming, and I occasionally throw out good emails that are nestled among particularly pernicious volleys of electronic detritus.
We make some simple machine learning software to pre-filter the most blatant offenders from my inbox. The source code of the production-ready C# class for the BayesianClassifier is available on one of my websites – http://joshwhitman.com
In the video, we have two timelapses of software development, an explanation of the math involved, an explanation of the architecture we’ll use when deploying to the production server, and we’ll get to...
published: 27 Feb 2020
Session 3: Naive Bayes' Classifier for Spam Filtering
Errata:
Dependent Events: Drawing of two cards from a deck of 52 cards one by one without replacement of the card into the deck.
- - -
In the 9th Minute:
We make a mistake in our comments in the slide number '7'. Here is the correction:
Note: we refer to the slide '7', again in the 11th minute.
P(Banana | Long, Sweet, Yellow)
= P(Long, Sweet, Yellow | Banana) / P(L,S,Y)
P(L,S,Y | B) = P(L,S,Y,B) / P(B)
Naïve Bayes := All the events (such as L, S, Y, B) are independent.
This implies:
P(L, S, Y, B) = P(L|B) * P(S|B) * P(Y|B) * P(B)
- - -
P(Orange | Long, Sweet, Yellow)
Answer: Whichever P() is higher
- - -
In the 23th Minute:
When we discard probability P(ham) from the denominator from the equation for "P(d6|ham)" is as follows:
P(d6|ham) = P(good, bad, very, bad, ham) / P(ham) # ...
[http://bit.ly/N-Bayes] How can we distinguish spam from non-spam with a Naive Bayes classifier? We estimate the priors and multiple Bernoulli distributions for...
[http://bit.ly/N-Bayes] How can we distinguish spam from non-spam with a Naive Bayes classifier? We estimate the priors and multiple Bernoulli distributions for each class. Also learn how Naive Bayes can misclassify its own training examples.
[http://bit.ly/N-Bayes] How can we distinguish spam from non-spam with a Naive Bayes classifier? We estimate the priors and multiple Bernoulli distributions for each class. Also learn how Naive Bayes can misclassify its own training examples.
Check out this great site on http://www.allspammedup.com/anti-spam/bayesian-spam-filtering/
How much of time and effort we end up wasting trying to sift throu...
Check out this great site on http://www.allspammedup.com/anti-spam/bayesian-spam-filtering/
How much of time and effort we end up wasting trying to sift through the mounds of spam that hits our mail boxes in order to get to the real mails that matter. With the help of Bayesian spam filter this can become a thing of the past. This is one of the most innovative and advanced of techniques that can help eliminate all sorts of spam. It was first heard of in the year 1998 but at that point of time no one actually paid much heed to it. Then, in 2002 people started to use these techniques to block spam from their inbox. Go on and allow Bayesian spam filter to help you wage a war against spam. Spam can be one of the worst things to our time management. It can eat into your time like nothing else ever can.
Check out this great site on http://www.allspammedup.com/anti-spam/bayesian-spam-filtering/
How much of time and effort we end up wasting trying to sift through the mounds of spam that hits our mail boxes in order to get to the real mails that matter. With the help of Bayesian spam filter this can become a thing of the past. This is one of the most innovative and advanced of techniques that can help eliminate all sorts of spam. It was first heard of in the year 1998 but at that point of time no one actually paid much heed to it. Then, in 2002 people started to use these techniques to block spam from their inbox. Go on and allow Bayesian spam filter to help you wage a war against spam. Spam can be one of the worst things to our time management. It can eat into your time like nothing else ever can.
When most people want to learn about Naive Bayes, they want to learn about the Multinomial Naive Bayes Classifier - which sounds really fancy, but is actually q...
When most people want to learn about Naive Bayes, they want to learn about the Multinomial Naive Bayes Classifier - which sounds really fancy, but is actually quite simple. This video walks you through it one step at a time and by the end, you'll no longer be naive about Naive Bayes!!!
Get the StatQuest Study Guide here: https://statquest.org/statquest-store/
For a complete index of all the StatQuest videos, check out:
https://statquest.org/video-index/
If you'd like to support StatQuest, please consider...
Buying my book, The StatQuest Illustrated Guide to Machine Learning:
PDF - https://statquest.gumroad.com/l/wvtmc
Paperback - https://www.amazon.com/dp/B09ZCKR4H6
Kindle eBook - https://www.amazon.com/dp/B09ZG79HXC
Patreon: https://www.patreon.com/statquest
...or...
YouTube Membership: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw/join
...a cool StatQuest t-shirt or sweatshirt:
https://shop.spreadshirt.com/statquest-with-josh-starmer/
...buying one or two of my songs (or go large and get a whole album!)
https://joshuastarmer.bandcamp.com/
...or just donating to StatQuest!
https://www.paypal.me/statquest
Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
https://twitter.com/joshuastarmer
0:00 Awesome song and introduction
1:08 Histograms and conditional probabilities
4:22 Classifying "Dear Friend"
7:33 Review of concepts
9:00 Classifying "Lunch Money x 5"
10:54 Pseudocounts
12:35 Why Naive Bayes is Naive
#statquest #naivebayes
When most people want to learn about Naive Bayes, they want to learn about the Multinomial Naive Bayes Classifier - which sounds really fancy, but is actually quite simple. This video walks you through it one step at a time and by the end, you'll no longer be naive about Naive Bayes!!!
Get the StatQuest Study Guide here: https://statquest.org/statquest-store/
For a complete index of all the StatQuest videos, check out:
https://statquest.org/video-index/
If you'd like to support StatQuest, please consider...
Buying my book, The StatQuest Illustrated Guide to Machine Learning:
PDF - https://statquest.gumroad.com/l/wvtmc
Paperback - https://www.amazon.com/dp/B09ZCKR4H6
Kindle eBook - https://www.amazon.com/dp/B09ZG79HXC
Patreon: https://www.patreon.com/statquest
...or...
YouTube Membership: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw/join
...a cool StatQuest t-shirt or sweatshirt:
https://shop.spreadshirt.com/statquest-with-josh-starmer/
...buying one or two of my songs (or go large and get a whole album!)
https://joshuastarmer.bandcamp.com/
...or just donating to StatQuest!
https://www.paypal.me/statquest
Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
https://twitter.com/joshuastarmer
0:00 Awesome song and introduction
1:08 Histograms and conditional probabilities
4:22 Classifying "Dear Friend"
7:33 Review of concepts
9:00 Classifying "Lunch Money x 5"
10:54 Pseudocounts
12:35 Why Naive Bayes is Naive
#statquest #naivebayes
This video were created by Amadeuz Ezrafel and Gagas Wicaksono S1 PTI Offering D '12, State University of Malang, to fulfill Final Project of Discrete Mathemati...
This video were created by Amadeuz Ezrafel and Gagas Wicaksono S1 PTI Offering D '12, State University of Malang, to fulfill Final Project of Discrete Mathematic lesson.
Many lack in this video, so please support us by giving like and comment. :)
If you want the complete coding in C++ please mail me: [email protected] ,
subject: Bayesian - Spam Filtering using C++
This video were created by Amadeuz Ezrafel and Gagas Wicaksono S1 PTI Offering D '12, State University of Malang, to fulfill Final Project of Discrete Mathematic lesson.
Many lack in this video, so please support us by giving like and comment. :)
If you want the complete coding in C++ please mail me: [email protected] ,
subject: Bayesian - Spam Filtering using C++
Learn how to solve any Bayes' Theorem problem. This tutorial first explains the concept behind Bayes' Theorem, where the equation comes from, and finally how t...
Learn how to solve any Bayes' Theorem problem. This tutorial first explains the concept behind Bayes' Theorem, where the equation comes from, and finally how to use the formula in an example. Bayes' Theorem is one of the most common equations covered in Statistics due to its numerous applications to the real world. It is also one of the most misunderstood theorems, but this video will help clear all of that up!
For more help, visit: https://theacetutors.com
For more videos/content, visit: https://theacetutors.com/blog
Sign up for tutoring: https://theacetutors.com/register
Learn how to solve any Bayes' Theorem problem. This tutorial first explains the concept behind Bayes' Theorem, where the equation comes from, and finally how to use the formula in an example. Bayes' Theorem is one of the most common equations covered in Statistics due to its numerous applications to the real world. It is also one of the most misunderstood theorems, but this video will help clear all of that up!
For more help, visit: https://theacetutors.com
For more videos/content, visit: https://theacetutors.com/blog
Sign up for tutoring: https://theacetutors.com/register
Check out Moneylender Professional (loan servicing software) - https://moneylenderprofessional.com
I have to manually filter over 500 spam emails every day. C...
Check out Moneylender Professional (loan servicing software) - https://moneylenderprofessional.com
I have to manually filter over 500 spam emails every day. Constantly clearing out the accreting digital refuse is hugely time consuming, and I occasionally throw out good emails that are nestled among particularly pernicious volleys of electronic detritus.
We make some simple machine learning software to pre-filter the most blatant offenders from my inbox. The source code of the production-ready C# class for the BayesianClassifier is available on one of my websites – http://joshwhitman.com
In the video, we have two timelapses of software development, an explanation of the math involved, an explanation of the architecture we’ll use when deploying to the production server, and we’ll get to see the classifier give us the probabilities of some test emails.
I’m happy to report that, instead of the usual 300ish messages waiting in my inbox every morning, this morning I was greeted by fewer than 30. That sure feels good!
I hope you enjoy the video. Subscribe for more like this. If you have any questions, ask in the comments and I’ll do my best to give you a meaningful answer.
Emojis by Twitter, Inc. / Twemoji on GitHub.com – CC-BY 4.0
Check out Moneylender Professional (loan servicing software) - https://moneylenderprofessional.com
I have to manually filter over 500 spam emails every day. Constantly clearing out the accreting digital refuse is hugely time consuming, and I occasionally throw out good emails that are nestled among particularly pernicious volleys of electronic detritus.
We make some simple machine learning software to pre-filter the most blatant offenders from my inbox. The source code of the production-ready C# class for the BayesianClassifier is available on one of my websites – http://joshwhitman.com
In the video, we have two timelapses of software development, an explanation of the math involved, an explanation of the architecture we’ll use when deploying to the production server, and we’ll get to see the classifier give us the probabilities of some test emails.
I’m happy to report that, instead of the usual 300ish messages waiting in my inbox every morning, this morning I was greeted by fewer than 30. That sure feels good!
I hope you enjoy the video. Subscribe for more like this. If you have any questions, ask in the comments and I’ll do my best to give you a meaningful answer.
Emojis by Twitter, Inc. / Twemoji on GitHub.com – CC-BY 4.0
Errata:
Dependent Events: Drawing of two cards from a deck of 52 cards one by one without replacement of the card into the deck.
- - -
In the 9th Minute:
We ...
Errata:
Dependent Events: Drawing of two cards from a deck of 52 cards one by one without replacement of the card into the deck.
- - -
In the 9th Minute:
We make a mistake in our comments in the slide number '7'. Here is the correction:
Note: we refer to the slide '7', again in the 11th minute.
P(Banana | Long, Sweet, Yellow)
= P(Long, Sweet, Yellow | Banana) / P(L,S,Y)
P(L,S,Y | B) = P(L,S,Y,B) / P(B)
Naïve Bayes := All the events (such as L, S, Y, B) are independent.
This implies:
P(L, S, Y, B) = P(L|B) * P(S|B) * P(Y|B) * P(B)
- - -
P(Orange | Long, Sweet, Yellow)
Answer: Whichever P() is higher
- - -
In the 23th Minute:
When we discard probability P(ham) from the denominator from the equation for "P(d6|ham)" is as follows:
P(d6|ham) = P(good, bad, very, bad, ham) / P(ham) # Eq 1
Since for Naive Bayes' Probability for Independent Events Assumption:
P(good, bad, very, bad, ham) = P(ham) . P(good|ham) . P(bad|ham) . P(very|ham) . P(bad|ham) # Eq 2
Now using Equations (1) and (2):
P(d6|ham) = P(ham) . P(good|ham) . P(bad|ham) . P(very|ham) . P(bad|ham) / P(ham)
This implies:
P(d6|ham) = P(good|ham) . P(bad|ham) . P(very|ham) . P(bad|ham)
Errata:
Dependent Events: Drawing of two cards from a deck of 52 cards one by one without replacement of the card into the deck.
- - -
In the 9th Minute:
We make a mistake in our comments in the slide number '7'. Here is the correction:
Note: we refer to the slide '7', again in the 11th minute.
P(Banana | Long, Sweet, Yellow)
= P(Long, Sweet, Yellow | Banana) / P(L,S,Y)
P(L,S,Y | B) = P(L,S,Y,B) / P(B)
Naïve Bayes := All the events (such as L, S, Y, B) are independent.
This implies:
P(L, S, Y, B) = P(L|B) * P(S|B) * P(Y|B) * P(B)
- - -
P(Orange | Long, Sweet, Yellow)
Answer: Whichever P() is higher
- - -
In the 23th Minute:
When we discard probability P(ham) from the denominator from the equation for "P(d6|ham)" is as follows:
P(d6|ham) = P(good, bad, very, bad, ham) / P(ham) # Eq 1
Since for Naive Bayes' Probability for Independent Events Assumption:
P(good, bad, very, bad, ham) = P(ham) . P(good|ham) . P(bad|ham) . P(very|ham) . P(bad|ham) # Eq 2
Now using Equations (1) and (2):
P(d6|ham) = P(ham) . P(good|ham) . P(bad|ham) . P(very|ham) . P(bad|ham) / P(ham)
This implies:
P(d6|ham) = P(good|ham) . P(bad|ham) . P(very|ham) . P(bad|ham)
[http://bit.ly/N-Bayes] How can we distinguish spam from non-spam with a Naive Bayes classifier? We estimate the priors and multiple Bernoulli distributions for each class. Also learn how Naive Bayes can misclassify its own training examples.
Check out this great site on http://www.allspammedup.com/anti-spam/bayesian-spam-filtering/
How much of time and effort we end up wasting trying to sift through the mounds of spam that hits our mail boxes in order to get to the real mails that matter. With the help of Bayesian spam filter this can become a thing of the past. This is one of the most innovative and advanced of techniques that can help eliminate all sorts of spam. It was first heard of in the year 1998 but at that point of time no one actually paid much heed to it. Then, in 2002 people started to use these techniques to block spam from their inbox. Go on and allow Bayesian spam filter to help you wage a war against spam. Spam can be one of the worst things to our time management. It can eat into your time like nothing else ever can.
When most people want to learn about Naive Bayes, they want to learn about the Multinomial Naive Bayes Classifier - which sounds really fancy, but is actually quite simple. This video walks you through it one step at a time and by the end, you'll no longer be naive about Naive Bayes!!!
Get the StatQuest Study Guide here: https://statquest.org/statquest-store/
For a complete index of all the StatQuest videos, check out:
https://statquest.org/video-index/
If you'd like to support StatQuest, please consider...
Buying my book, The StatQuest Illustrated Guide to Machine Learning:
PDF - https://statquest.gumroad.com/l/wvtmc
Paperback - https://www.amazon.com/dp/B09ZCKR4H6
Kindle eBook - https://www.amazon.com/dp/B09ZG79HXC
Patreon: https://www.patreon.com/statquest
...or...
YouTube Membership: https://www.youtube.com/channel/UCtYLUTtgS3k1Fg4y5tAhLbw/join
...a cool StatQuest t-shirt or sweatshirt:
https://shop.spreadshirt.com/statquest-with-josh-starmer/
...buying one or two of my songs (or go large and get a whole album!)
https://joshuastarmer.bandcamp.com/
...or just donating to StatQuest!
https://www.paypal.me/statquest
Lastly, if you want to keep up with me as I research and create new StatQuests, follow me on twitter:
https://twitter.com/joshuastarmer
0:00 Awesome song and introduction
1:08 Histograms and conditional probabilities
4:22 Classifying "Dear Friend"
7:33 Review of concepts
9:00 Classifying "Lunch Money x 5"
10:54 Pseudocounts
12:35 Why Naive Bayes is Naive
#statquest #naivebayes
This video were created by Amadeuz Ezrafel and Gagas Wicaksono S1 PTI Offering D '12, State University of Malang, to fulfill Final Project of Discrete Mathematic lesson.
Many lack in this video, so please support us by giving like and comment. :)
If you want the complete coding in C++ please mail me: [email protected] ,
subject: Bayesian - Spam Filtering using C++
Learn how to solve any Bayes' Theorem problem. This tutorial first explains the concept behind Bayes' Theorem, where the equation comes from, and finally how to use the formula in an example. Bayes' Theorem is one of the most common equations covered in Statistics due to its numerous applications to the real world. It is also one of the most misunderstood theorems, but this video will help clear all of that up!
For more help, visit: https://theacetutors.com
For more videos/content, visit: https://theacetutors.com/blog
Sign up for tutoring: https://theacetutors.com/register
Check out Moneylender Professional (loan servicing software) - https://moneylenderprofessional.com
I have to manually filter over 500 spam emails every day. Constantly clearing out the accreting digital refuse is hugely time consuming, and I occasionally throw out good emails that are nestled among particularly pernicious volleys of electronic detritus.
We make some simple machine learning software to pre-filter the most blatant offenders from my inbox. The source code of the production-ready C# class for the BayesianClassifier is available on one of my websites – http://joshwhitman.com
In the video, we have two timelapses of software development, an explanation of the math involved, an explanation of the architecture we’ll use when deploying to the production server, and we’ll get to see the classifier give us the probabilities of some test emails.
I’m happy to report that, instead of the usual 300ish messages waiting in my inbox every morning, this morning I was greeted by fewer than 30. That sure feels good!
I hope you enjoy the video. Subscribe for more like this. If you have any questions, ask in the comments and I’ll do my best to give you a meaningful answer.
Emojis by Twitter, Inc. / Twemoji on GitHub.com – CC-BY 4.0
Errata:
Dependent Events: Drawing of two cards from a deck of 52 cards one by one without replacement of the card into the deck.
- - -
In the 9th Minute:
We make a mistake in our comments in the slide number '7'. Here is the correction:
Note: we refer to the slide '7', again in the 11th minute.
P(Banana | Long, Sweet, Yellow)
= P(Long, Sweet, Yellow | Banana) / P(L,S,Y)
P(L,S,Y | B) = P(L,S,Y,B) / P(B)
Naïve Bayes := All the events (such as L, S, Y, B) are independent.
This implies:
P(L, S, Y, B) = P(L|B) * P(S|B) * P(Y|B) * P(B)
- - -
P(Orange | Long, Sweet, Yellow)
Answer: Whichever P() is higher
- - -
In the 23th Minute:
When we discard probability P(ham) from the denominator from the equation for "P(d6|ham)" is as follows:
P(d6|ham) = P(good, bad, very, bad, ham) / P(ham) # Eq 1
Since for Naive Bayes' Probability for Independent Events Assumption:
P(good, bad, very, bad, ham) = P(ham) . P(good|ham) . P(bad|ham) . P(very|ham) . P(bad|ham) # Eq 2
Now using Equations (1) and (2):
P(d6|ham) = P(ham) . P(good|ham) . P(bad|ham) . P(very|ham) . P(bad|ham) / P(ham)
This implies:
P(d6|ham) = P(good|ham) . P(bad|ham) . P(very|ham) . P(bad|ham)
Naive Bayes classifiers work by correlating the use of tokens (typically words, or sometimes other things), with spam and non-spam e-mails and then using Bayes' theorem to calculate a probability that an email is or is not spam.
Naive Bayes spam filtering is a baseline technique for dealing with spam that can tailor itself to the email needs of individual users and give low false positive spam detection rates that are generally acceptable to users. It is one of the oldest ways of doing spam filtering, with roots in the 1990s.
History
The first known mail-filtering program to use a naive Bayes classifier was Jason Rennie's ifile program, released in 1996. The program was used to sort mail into folders. The first scholarly publication on Bayesian spam filtering was by Sahami et al. in 1998. That work was soon thereafter deployed in commercial spam filters. However, in 2002 Paul Graham greatly decreased the false positive rate, so that it could be used on its own as a single spam filter.
Microsoft’s Mail uses Bayesian spam filtering to protect users from suspicious emails ... This waiting period is because the spam filters aren’t perfect and may flag a valid email as suspicious.