NOTE: This StatQuest is sponsored by JADBIO. Just Add Data, and their automatic machine learning algorithms will do all of the work for you. For more details, see: bit.ly/3bxtheb BAM! Corrections: 3:42 I said 10 grams of popcorn, but I should have said 20 grams of popcorn given that they love Troll 2. Support StatQuest by buying my books The StatQuest Illustrated Guide to Machine Learning, The StatQuest Illustrated Guide to Neural Networks and AI, or a Study Guide or Merch!!! statquest.org/statquest-store/
4 weeks back I had no idea what is machine learning, but your videos have really made a difference in my life, they are all so clearly explained and fun to watch, I just got a job and I mentioned some of the learnings I had from your channel, I am grateful for your contribution in my life.
Im at the point where my syllabus does not require me to look into all of this but im just having too much fun learning with you. Im glad i took this course up to find your videos
This is by far my favorite educational RUclips channel. Everything is explained in a simple, practical and fun way. The videos are full of positive vibes just from the beginning with the silly song entry. I love the catch phrases. Statquest is addictive!
WOOOOOOW. I watched every video of yours, recommended in the description of this video, and now this video. Everything makes much more sense now. It helped me a lot to undersand the Gaussian Naive Bayes algorithm implemented and available from scikit-learn for applications in machine learning. Just awesome. Thank you!!!
Thank you Josh. You deserve all the praises. I have been struggling with a lot of the concepts on traditional classic text books as they tend to "jump" quite a lot. You channel brings all of them to life vividly. This is my go to reference source now.
I am a beginner in Machine Learning field, and your channel helped me alot, almost went through all the videos, very nice way of explaining. Really appreciate you for making these videos and helping everyone. You just saved me ... Thank you very much...
Hi, Josh. Thank you so much for all the exceptional content from your channel. Your work is amazing. I'm a professor in Brazil of Computer Science and ML and your videos have been supporting me a lot. You're an inspiration for me. Best.
Thank you for the prompt response. I’m fairly new to Stats. But this video prompted me to do a lot more research and I’m finally confident on how you got to the result. Thank you for your videos. They are so helpful
It's amazing! Thank you so much ! Our professor let us self-teach the Gaussian naive bayes and I absolutely don't understand her slides with many many math equations. Thanks again for your vivid videos !!
This is crazy I went to school for Applied Mathematics and it never crossed my mind that what I learned was machine learning as chatgpt came into the lime light I started looking into it and almost everything I've learned so far is basically everything I've learned before but in a different context. My mind is just blown that I was assuming ML was something unattainable for me and it turns out I've been doing it for years
Daym, your videos are so good at explaining complicated ideas!! Like holy shoot, I am going to use this, multiple predictors ideas to figure out the ending of inception, Was it dream, or was it not a dream!
contents are excellent and also i love your intro quite a lot (its super impressive for me) btw. thanking for doing this at the fisrt place as a beginner some concepts are literally hard to understand but after watching your videos things are a lot better than before. Thanks :)
In Stats Playlist, we used following notation for P( Data | Model ) for probability & L(Model | Data) for likelihood; Here we are writing likelihood as L(popcorn=20 | Loves) which I guess L( Data | Model );
Unfortunately the notation is somewhat flexible and inconsistent - not just in my videos, but in the the field in general. The important thing is to know that likelihoods are always the y-axis values, and probabilities are the areas.
@@statquest understood; somewhere in the playlist you mentioned that likelihood is relative probability; and I guess this neatly summaries how likelihood and probability
@@statquest I think you should explain some formula briefly. Like in Naive Bayes algorithm, you'd better explain why P(N)*P(Dear|N)*P(Friend|N)=P(N|Dear,Friend). I use GPT to finally understand it.
@@CyberGimen I've got a whole video about that here: ruclips.net/video/9wCnvr7Xw4E/видео.html However, the reason I don't mention it in this video is that it's actually not critical to using the method.
Glad you liked it. It's a new feature that RUclips just rolled out so I've spent the past day (and will spend the next few days) adding it to my videos.
Hey Josh I hope you are having a wonderful day, I was searching for a video on " Gaussian mixture model " on your channel but couldn't find one, I have a request for that video since the concept is a bit complicated elsewhere Also btw your videos enabled to get one of the highest scores in the test conducted recently in my college, all thanks to you Josh, you are awesome
Great style of teaching & also thank you so much for such a great video (Note : I have bought your book "The StatQuest illustrated guide to machine learning") 😃
Josh. I love you your videos. I've been following your channel for a while. Your videos are absolutely great! Would you consider covering more of Bayesian statistics in the future?
These videos are extremely valuable, thank you for sharing them. I feel that they really help to illuminate the material. Quick question though: where do you get the different probabilities, like for popcorn, soda pop, and candy? How do we calculate those in this context? Do you use the soda a person drinks and divide it by the total soda, and same with popcorn, and candy?
What time point are you asking about (in minutes and seconds). The only probabilities we use in this video are if someone loves or doesn't love troll 2. Everything else is a likelihood, which is just a y-axis coordinate.
A really comprehensive video. Thank you! Sir, I have some questions about the conditions when applying this algo: 1. Is it compulsory that all features contain continuous value? 2. What happens if a feature doesn't have gaussian distribution? Is it worth to apply this algo? 3. If that, I will find a function that makes that feature have gaussian distribution. Can it work? And also, Do u plan to do a video about Bernoulli Naive Bayes?
1. No - you can mix things up. I illustrate this in my book. 2. You can use other distributions 3. No need, just use the other distribution. 4. Not in the short term.
Thanks for the great video! I would just like to point out that in my opinion if you are talking about log() when the base is e, it is easier (and more correct) to write ln().
Josh, a question about the formulation of Bayes' Theorem, especially considering the likelihood. For Naive Bayes, the formula is: P(class | X) = P(class) * P(X | class), in which the last term. is the likelihood In your video, you represented the likelihood as L, so that, apparently, the formula would be: P(No Love | X) = P(No Love) * L(X | No Love) (1) Is my assumption correct? Is it just a change of letters to mean the same thing? (2) Or is there any other math under the hoods? For example, something like: P(X | class) = L(No Love | X) Thanks in advance.
When I use the notation "L(something)" for "likelihood", I mean that we want the corresponding y-axis coordinate for that something. However, not everyone uses that notation. Some put p(something) and you have to figure out from the context whether or not they are talking about a likelihood (y-axis coordinate) or, potentially, a probability (since "p" often refers to "probability"). So, if you use my notation, then you are correct, you get: P(No Love | X) = P(No Love) * L(X | No Love)
Hi - another great explanation! I wonder what would be the result if you normalise the probabilies of the 3 values. - Would it affect the outcome of the example in this video? - Which areas of values are affected: different outcomes with non-normalised and normalised distributions (=probability or likelihood here)?
@@statquest Hi, that only make sense with real data. Without that, only juggling with equations and abstract parameters, the thing is not enough 'visual', IMO. Though, could run through the calculations with e.g. 2x scale, 10x scale and 100x scale... Maybe, when I have free few hours.
Great work ! In 8:11 How can we use cross validation with Gaussian Naive Bayes? I have watched the Cross validation video but I still can't figure out how to employ cross validation to know that candy can make the best classification.
to apply cross validation, we divide the training data into different groups - then we use all of the groups, minus 1, to create a gaussian naive bayes model. Then we use that model to make predictions based on the last group. Then we repeat, each time using a different group to test the model.
Dear Mr. Josh, I have taken another course have the following equation for the probability of Naive Bayes, P( Loves Troll 2 | new data ) = [ P( new data | Loves Troll 2 ) * P( Loves Troll 2 ) ] / P( new data) P( new data ) called marginal likelihood, and P( new data | Loves Troll 2 ) called likelihood And then, the way to calculate marginal likelihood and likelihood is to calculate the probability nearby the data at a certain distance, and the distance is adjustable while you are building the algorithm. For instance, there is a circle in which the center is new data and you can adjust the radius if your data is 2-D data. After watching both courses, I am wondering how can these two equations be equivalent? I deeply appreciate your time for answering my question. Sincerely, Gavin
The marginal likelihood is often omitted because both p(Loves Troll 2 | data) and p(Does not love Troll 2 | data) are divided by it. In other words, the only thing that makes p(Loves Troll 2 | data) different from p(Does not love Troll 2 | data) is what is in the numerator. And because it is usually really hard to calculate the marginal likelihood, we just omit it because it will not change the results.
3:38, shouldn’t the notation be L(Loves | popcorn=20), since we’re given that he eats 20g of popcorn, how likely is that sample generated from the Loves distribution. Isn’t that right?
Can we also say that this person can be an outlier? Because of having very high likelihood of popcorn and soda pop scores given that he likes troll 2 and only but high variance according to 3rd category we can also say consider him under the outlier category, can't we? Can you clear this doubt for me, please! And also thanks a lot for your effort and work..
Maybe. It depends on how much data we have in the training dataset - because that will define how confident we are that we have correctly modeled the two categories.
@@statquest Yes! If the training dataset contains a good enough number of data then we can calculate the margin of error too at various confidence levels with the given sample size and present our output. Thank you!
Hey JOSH Thanks for making such amazing video. Keep up the work. I just have a quick question if you don't mind. I can't understand how you got the likelihood eg: L(soda = 500 | LOVES) how you calculating that value.
We plugged the mean and standard deviation of soda pot for people that loved Troll2 into the equation for a normal curve and then determined the y-axis coordinate when the x-axis value = 500.
No, probability is the area under the curve and those are defined such that the total area under the curve is always 1. For details, see: ruclips.net/video/pYxNSUDSFH4/видео.html
@@statquest Dear Dr. Starmer, Thank you for your reply. I have another follow-up question regarding the calculation of probabilities for continuous random variables (i.e. what this video is about). From my understanding, when we have discrete random variables, the probability of a given outcome P(Y=y|X1,X2,..Xn) is proportional to the product of the probabilities of the individual variables given the outcome, times the prior probability (assuming conditional independence). i.e. P(Y=y) * the product of P(Xi=xi | Y=y) This makes sense to me, because the result is a probability value between 0 and 1. However, in the case of continuous random variables, the probability of a given outcome is zero, so we instead calculate the likelihood of the outcome. This means that the product of the individual likelihoods is no longer a probability value between 0 and 1. Is this correct? What I mean is: P(Y=y) * the product of L(Xi=xi | Y=y) is not guaranteed to be a value between 0 and 1. Thank you for your expertise and for being such a valuable educator. 💖
@@kirilblazevski8329 That's correct, with the continuous version, we do not end up with probabilities. However, if you saw my video on the discrete version of Naive Bayes ( ruclips.net/video/O2L2Uv9pdDA/видео.html ) you'll notice that I call the results "scores" instead of probabilities. The reason for this is that in both cases (discrete and continuous), to get the correct probabilities for the results, you need to divide the results (what I call "scores") by the sum of the scores for the two possibilities. By doing this, you normalize the scores for the two possibilities so that they will add up to 1.
A nice video on Gaussian Naive Bayes Classification model. Well done! But I have a quick question for you, Josh. I only understand that Lim ln(x) as x approaches o is negative infinity. How is the Natural log of a really small unknown number very close to zero assumed to be equal to -115 and -33.6 as in the case of L(candy=25|Love Troll 2) and L(popcorn=20|does not Love Troll 2) respectively? What measure was used to determine these values?
Hello! Does it matter if the data in one of the columns (say popcorn) is not normally distributed? Or should the assumption be that we will have a large enough sample size to use the central limit theorem? Thanks for all of your videos! I love them and can’t wait for your book to be delivered (just ordered it yesterday).
It doesn't matter how the data are distributed. As long as we can calculate the likelihoods, we are good to go. BAM! :) And thank you so much for supporting StatQuest!!! TRIPLE BAM!!! :)
Hi Josh.. Thank you very much for your tutorial video. I am a big fan sir I have a clarification. The P(Love Troll) or P (No love Troll) given the 3 variables - here in this example - we multiply the Prior Probability of the class with the likelihood of the variables given the class ... However as per Bayes's Theorem, it is also divided by the probability (or likelihood) of the variables... which is not done in this tutorial, same with the Naive Bayes "clearly explained" tutorial... I am sorry if have asked something "naive" :)
You have hit on one of the reasons I do not mention bayes' theorem in either of these videos. These methods are called "naive bayes", but they only use the numerator of that equation, because calculating the denominator would be hard to do. That said, the denominator would be the same for both classes, so it scales all "scores" by the same amount. And since we are only interested in the highest relative score, we can omit the denominator and still get the job done.
Hi Josh, I wanted to know as to how do we get the likelihood from the y axis ?? lets say in the video at 4:12 you get the likelihood from the y axis for drinks 500 ml of soda given the person loves troll 2 to be 0.004. So how are we getting 0.004 ?
That distribution has a mean = 500 and standard deviation = 100. So I plug those numbers, plus x = 500 into the equation for a normal distribution (see ruclips.net/video/Dn6b9fCIUpM/видео.html ) and the value that comes out is 0.004.
Thanks for this super clear explanation. Why would we prefer this method for classification over a gradient boosting algorithm? When we have too few samples?
![Seungmin "그렇게, 천천히, 우리(As we are)" | [Stray Kids : SKZ-PLAYER]](http://i.ytimg.com/vi/kAzmhLHePqU/mqdefault.jpg)
NOTE: This StatQuest is sponsored by JADBIO. Just Add Data, and their automatic machine learning algorithms will do all of the work for you. For more details, see: bit.ly/3bxtheb BAM!
Corrections:
3:42 I said 10 grams of popcorn, but I should have said 20 grams of popcorn given that they love Troll 2.
Support StatQuest by buying my books The StatQuest Illustrated Guide to Machine Learning, The StatQuest Illustrated Guide to Neural Networks and AI, or a Study Guide or Merch!!! statquest.org/statquest-store/
website not working?
@@phildegreat Thanks! The site is back up.
8:15 There's a minor error in the slide 'help use decide' .
You really are a great teacher.Wish I could Meet you in person some day.
4 weeks back I had no idea what is machine learning, but your videos have really made a difference in my life, they are all so clearly explained and fun to watch, I just got a job and I mentioned some of the learnings I had from your channel, I am grateful for your contribution in my life.
Happy to help!
Congratulations!!
That is a HUGE help my friend, congrats.. !!
Im at the point where my syllabus does not require me to look into all of this but im just having too much fun learning with you. Im glad i took this course up to find your videos
Hooray! :)
Following your channel for over 6 months now sir, your explanations are truly amazing..
Thank you very much! :)
This is by far my favorite educational RUclips channel.
Everything is explained in a simple, practical and fun way.
The videos are full of positive vibes just from the beginning with the silly song entry. I love the catch phrases.
Statquest is addictive!
Thank you very much! :)
I have watched over 2-3 hours of lecture about Gaussian Naive Bayes. Now is when I feel my understanding is complete.
Hooray!
If I remember all the best educator's name on RUclips, you always come at the beginning! You are a flawless genius!
Thank you! 😃
WOOOOOOW. I watched every video of yours, recommended in the description of this video, and now this video. Everything makes much more sense now. It helped me a lot to undersand the Gaussian Naive Bayes algorithm implemented and available from scikit-learn for applications in machine learning. Just awesome. Thank you!!!
Wow, thanks!
Thank you Josh. You deserve all the praises. I have been struggling with a lot of the concepts on traditional classic text books as they tend to "jump" quite a lot. You channel brings all of them to life vividly. This is my go to reference source now.
Awesome! I'm glad my videos are helpful.
My little knowledge about machine learning could not be derived without your tutorials. Thank you very much
Glad I could help!
I am a beginner in Machine Learning field, and your channel helped me alot, almost went through all the videos, very nice way of explaining. Really appreciate you for making these videos and helping everyone. You just saved me ... Thank you very much...
Thank you very much! :)
One of the best channel for learners that the world can offer..
Thank you!
Hi, Josh.
Thank you so much for all the exceptional content from your channel.
Your work is amazing.
I'm a professor in Brazil of Computer Science and ML and your videos have been supporting me a lot.
You're an inspiration for me.
Best.
Muito obrigado!
amazing kowledge with incredible communication skills..world will change if every student has such great teacher
Thank you!
Thank you for the prompt response. I’m fairly new to Stats. But this video prompted me to do a lot more research and I’m finally confident on how you got to the result. Thank you for your videos. They are so helpful
Glad it was helpful!
It's amazing! Thank you so much !
Our professor let us self-teach the Gaussian naive bayes and I absolutely don't understand her slides with many many math equations. Thanks again for your vivid videos !!
Glad it was helpful!
Literally the best video ever on this.
Thank you!
This is the only lecture that makes me feel not stupid...
:)
Sir, this playlist is a one-stop solution for quick interview preparations. Thanks a lot sir.
Good luck with your interviews! :)
This is crazy I went to school for Applied Mathematics and it never crossed my mind that what I learned was machine learning as chatgpt came into the lime light I started looking into it and almost everything I've learned so far is basically everything I've learned before but in a different context. My mind is just blown that I was assuming ML was something unattainable for me and it turns out I've been doing it for years
bam!
same applied math undergraduate student who switched to AI field as a postgraduate student now🙂
this was the best explanation i've ever seen in my life, (i'm not even a english native speaker, i'm brazilian lol)
Muito obrigado! :)
you explained much clearer than my lecturer in ML lecture.
Thanks!
Great video! If people are willing to spend time on videos like this rather than Tiktok, the wold would be a much better place.
Thank you very much! :)
This channel has helped me so much during my studies 🎉
Happy to hear that!
These videos are amazing !!! Truly a survival pack for my DS class👍
Bam! :)
Daym, your videos are so good at explaining complicated ideas!! Like holy shoot, I am going to use this, multiple predictors ideas to figure out the ending of inception, Was it dream, or was it not a dream!
BAM! :)
Your videos and voice make ML and statistics fun to learn. :)
Glad you like them!
These gloriously wierd examples really are needed to understand a concept
Thanks!
contents are excellent and also i love your intro quite a lot (its super impressive for me) btw. thanking for doing this at the fisrt place as a beginner some concepts are literally hard to understand but after watching your videos things are a lot better than before. Thanks :)
I'm glad my videos are helpful! :)
This channel should have 2.74M subscribers instead of 274K.
One day I hope that happens! :)
This video on Gaussian Naive Bayes has been very well explained. Thanks a lot.😊
Most welcome 😊
superb cool explanation. I am big fan of your explanation. Once I went through your explanation, I don't want any further reference for that topic.
Thanks!
This series is helping me so much with my dissertation, thank you!!
Awesome and good luck with your disertation!
In Stats Playlist, we used following notation for P( Data | Model ) for probability & L(Model | Data) for likelihood;
Here we are writing likelihood as L(popcorn=20 | Loves) which I guess L( Data | Model );
Unfortunately the notation is somewhat flexible and inconsistent - not just in my videos, but in the the field in general. The important thing is to know that likelihoods are always the y-axis values, and probabilities are the areas.
@@statquest understood; somewhere in the playlist you mentioned that likelihood is relative probability; and I guess this neatly summaries how likelihood and probability
I just had the exact same question when I started writing the expression in my notebook. I am more acquainted with the L(Model | Data) notation.
Thank you, You have made the theory concrete and visible!
Thanks!
How do people come up with these crazy ideas? it's amazing, thanks a lot for another fantastic video
Thank you again!
Bam! I love your teaching style!!!
Thanks!
@@statquest I think you should explain some formula briefly. Like in Naive Bayes algorithm, you'd better explain why P(N)*P(Dear|N)*P(Friend|N)=P(N|Dear,Friend). I use GPT to finally understand it.
@@CyberGimen I've got a whole video about that here: ruclips.net/video/9wCnvr7Xw4E/видео.html However, the reason I don't mention it in this video is that it's actually not critical to using the method.
You have really helped me a lot. Thanks Sir. May you prosper more and keep helping students who cant afford paid content :)
Thank you! :)
The demarcation of topics in the seek bar is useful and helpful. Nice addition.
Glad you liked it. It's a new feature that RUclips just rolled out so I've spent the past day (and will spend the next few days) adding it to my videos.
@@statquest We really appreciate all your dedication into the channel!
It's 100% awesomeness :)
@@anitapallenberg690 Hooray! Thank you! :)
The world needs more Joshuas!
Thanks! :)
Why the fuck does this video make it look so easy and makes 100 percent sense?
i promise i will join the membership and buy your products when i get a job... BAM!!!
Hooray! Thank you very much for your support!
Hey Josh I hope you are having a wonderful day, I was searching for a video on " Gaussian mixture model " on your channel but couldn't find one, I have a request for that video since the concept is a bit complicated elsewhere
Also btw your videos enabled to get one of the highest scores in the test conducted recently in my college, all thanks to you Josh, you are awesome
Thanks! I'll keep that topic in mind.
I'm Having great time watching Ur videos ❤️
Thanks!
Great style of teaching & also thank you so much for such a great video (Note : I have bought your book "The StatQuest illustrated guide to machine learning") 😃
Thank you so much for supporting StatQuest!
awesome stuff for real
Thank you!
I'm a simple man, I watch statquests in the nights, leave a like and go chat about it with chatgpt.That's it.
bam! :)
Your videos are really great !! my prof made it way harder!!
Thanks!
Thank you for another excellent Statquest !~
Bam! :)
Your video just helped me a lot !
Glad it helped!
+5000 for using an example as obscure and as obscene as Troll 2.
:)
Tqsm Sir for the Very Valuable Information
Thanks! :)
Thanks for the video !! it was very helpful and easy to understand
Glad it was helpful!
Josh. I love you your videos. I've been following your channel for a while. Your videos are absolutely great!
Would you consider covering more of Bayesian statistics in the future?
I'll keep it in mind.
Thank you Josh for another great video! Also, this (and other vids) makes think I should watch Troll 2, just to tick that box.
Ha! Let me know what you think!
Another great tutorial, thank you!
Thanks!
Best video i have ever seen
:)
can't wait for your channel to BAAM! going worldwide!!
Me too!!
Super awesome, thank you. Useful for my Intro to Artificial Intelligence course.
Glad it was helpful!
These videos are extremely valuable, thank you for sharing them. I feel that they really help to illuminate the material.
Quick question though: where do you get the different probabilities, like for popcorn, soda pop, and candy? How do we calculate those in this context? Do you use the soda a person drinks and divide it by the total soda, and same with popcorn, and candy?
What time point are you asking about (in minutes and seconds). The only probabilities we use in this video are if someone loves or doesn't love troll 2. Everything else is a likelihood, which is just a y-axis coordinate.
A really comprehensive video. Thank you!
Sir, I have some questions about the conditions when applying this algo:
1. Is it compulsory that all features contain continuous value?
2. What happens if a feature doesn't have gaussian distribution? Is it worth to apply this algo?
3. If that, I will find a function that makes that feature have gaussian distribution. Can it work?
And also, Do u plan to do a video about Bernoulli Naive Bayes?
1. No - you can mix things up. I illustrate this in my book.
2. You can use other distributions
3. No need, just use the other distribution.
4. Not in the short term.
So great, this video so helpful
Glad it was helpful!
Thanks for the great video!
I would just like to point out that in my opinion if you are talking about log() when the base is e, it is easier (and more correct) to write ln().
In statistics, programming and machine learning, "ln()" is written "log()", so I'm just following the conventions used in the field.
BAM! Someone is going to pass the exam this semester .
Hooray!
Love your channel
Thanks!
Looks like I have to check out the quests before getting to this one😂
:)
Love the explaination BAM!
BAM! :)
Hi, Josh. Troll 2 is a good movie... Thanks
bam!
Hey Josh, Thank you for making these amazing videos. Please make a video on the "Bayesian Networks" too.
I'll keep it in mind.
Troll 2 is an awesome classic, and should not be up for debate. =)
Ha! :)
Awesome as always
Thanks again! :)
Great so much Thanks!
You're welcome!
Great video!
Thanks!
Finally worked up to the Gaussian Naive Bayes. BAM! "If you are not familiar with
..." :(
You can do it! :)
StatQuest made me lose my anxiety for statistics. It's truly brilliant, just start with the next video!
BAM! :)
Josh, a question about the formulation of Bayes' Theorem, especially considering the likelihood.
For Naive Bayes, the formula is:
P(class | X) = P(class) * P(X | class), in which the last term. is the likelihood
In your video, you represented the likelihood as L, so that, apparently, the formula would be:
P(No Love | X) = P(No Love) * L(X | No Love)
(1) Is my assumption correct? Is it just a change of letters to mean the same thing?
(2) Or is there any other math under the hoods?
For example, something like: P(X | class) = L(No Love | X)
Thanks in advance.
When I use the notation "L(something)" for "likelihood", I mean that we want the corresponding y-axis coordinate for that something. However, not everyone uses that notation. Some put p(something) and you have to figure out from the context whether or not they are talking about a likelihood (y-axis coordinate) or, potentially, a probability (since "p" often refers to "probability"). So, if you use my notation, then you are correct, you get: P(No Love | X) = P(No Love) * L(X | No Love)
I dont even know why there is people disliking this video!!
It's always a mystery. :)
I love you bro !
Thanks!
Hi - another great explanation!
I wonder what would be the result if you normalise the probabilies of the 3 values.
- Would it affect the outcome of the example in this video?
- Which areas of values are affected: different outcomes with non-normalised and normalised distributions (=probability or likelihood here)?
Interesting questions! You should try it out and see what you get.
@@statquest Hi, that only make sense with real data. Without that, only juggling with equations and abstract parameters, the thing is not enough 'visual', IMO. Though, could run through the calculations with e.g. 2x scale, 10x scale and 100x scale... Maybe, when I have free few hours.
Great work ! In 8:11 How can we use cross validation with Gaussian Naive Bayes? I have watched the Cross validation video but I still can't figure out how to employ cross validation to know that candy can make the best classification.
to apply cross validation, we divide the training data into different groups - then we use all of the groups, minus 1, to create a gaussian naive bayes model. Then we use that model to make predictions based on the last group. Then we repeat, each time using a different group to test the model.
Can you talk about Kernel estimation in the future?? Bam!
I will consider it.
BAM! thanks, Josh! It would be amazing if you can make a StatQuest concerning A/B testing :)
It's on the to-do list. :)
Dear Mr. Josh,
I have taken another course have the following equation for the probability of Naive Bayes,
P( Loves Troll 2 | new data ) = [ P( new data | Loves Troll 2 ) * P( Loves Troll 2 ) ] / P( new data)
P( new data ) called marginal likelihood,
and P( new data | Loves Troll 2 ) called likelihood
And then, the way to calculate marginal likelihood and likelihood is to calculate the probability nearby the data at a certain distance, and the distance is adjustable while you are building the algorithm. For instance, there is a circle in which the center is new data and you can adjust the radius if your data is 2-D data.
After watching both courses, I am wondering how can these two equations be equivalent?
I deeply appreciate your time for answering my question.
Sincerely,
Gavin
The marginal likelihood is often omitted because both p(Loves Troll 2 | data) and p(Does not love Troll 2 | data) are divided by it. In other words, the only thing that makes p(Loves Troll 2 | data) different from p(Does not love Troll 2 | data) is what is in the numerator. And because it is usually really hard to calculate the marginal likelihood, we just omit it because it will not change the results.
Thank you josh your videos are amazing! HoW to buy study guides from statquest
See: statquest.gumroad.com/
Thanks for the awesome video..
You bet!
3:38, shouldn’t the notation be L(Loves | popcorn=20), since we’re given that he eats 20g of popcorn, how likely is that sample generated from the Loves distribution. Isn’t that right?
The notation in the video is most common, however, the notation doesn't really matter as long as it is clear that we want the y-axis coordinate.
Amazing videos. The beep boop sound reminds me of squid games
Maybe they got the sound from my video! :)
Can we also say that this person can be an outlier? Because of having very high likelihood of popcorn and soda pop scores given that he likes troll 2 and only but high variance according to 3rd category we can also say consider him under the outlier category, can't we? Can you clear this doubt for me, please! And also thanks a lot for your effort and work..
Maybe. It depends on how much data we have in the training dataset - because that will define how confident we are that we have correctly modeled the two categories.
@@statquest
Yes!
If the training dataset contains a good enough number of data then we can calculate the margin of error too at various confidence levels with the given sample size and present our output.
Thank you!
Hey JOSH Thanks for making such amazing video. Keep up the work. I just have a quick question if you don't mind.
I can't understand how you got the likelihood eg: L(soda = 500 | LOVES) how you calculating that value.
We plugged the mean and standard deviation of soda pot for people that loved Troll2 into the equation for a normal curve and then determined the y-axis coordinate when the x-axis value = 500.
Excellent explanation. Any NLP series coming up ? Struggling to find good resources.
I'm working on Neural Networks right now.
@@statquest it's going to be BAM!!
Since the likelihood can be greater than 1, doesn't that mean that we could get probability that is greater than 1?
No, probability is the area under the curve and those are defined such that the total area under the curve is always 1. For details, see: ruclips.net/video/pYxNSUDSFH4/видео.html
@@statquest
Dear Dr. Starmer,
Thank you for your reply. I have another follow-up question regarding the calculation of probabilities for continuous random variables (i.e. what this video is about).
From my understanding, when we have discrete random variables, the probability of a given outcome P(Y=y|X1,X2,..Xn) is proportional to the product of the probabilities of the individual variables given the outcome, times the prior probability (assuming conditional independence).
i.e. P(Y=y) * the product of P(Xi=xi | Y=y)
This makes sense to me, because the result is a probability value between 0 and 1.
However, in the case of continuous random variables, the probability of a given outcome is zero, so we instead calculate the likelihood of the outcome. This means that the product of the individual likelihoods is no longer a probability value between 0 and 1. Is this correct?
What I mean is: P(Y=y) * the product of L(Xi=xi | Y=y) is not guaranteed to be a value between 0 and 1.
Thank you for your expertise and for being such a valuable educator. 💖
@@kirilblazevski8329 That's correct, with the continuous version, we do not end up with probabilities. However, if you saw my video on the discrete version of Naive Bayes ( ruclips.net/video/O2L2Uv9pdDA/видео.html ) you'll notice that I call the results "scores" instead of probabilities. The reason for this is that in both cases (discrete and continuous), to get the correct probabilities for the results, you need to divide the results (what I call "scores") by the sum of the scores for the two possibilities. By doing this, you normalize the scores for the two possibilities so that they will add up to 1.
@@statquest Now I understand what I was missing. Thank you for clarifying, I really appreciate it!!
A nice video on Gaussian Naive Bayes Classification model. Well done! But I have a quick question for you, Josh. I only understand that Lim ln(x) as x approaches o is negative infinity. How is the Natural log of a really small unknown number very close to zero assumed to be equal to -115 and -33.6 as in the case of L(candy=25|Love Troll 2) and L(popcorn=20|does not Love Troll 2) respectively? What measure was used to determine these values?
log(1.1*10^-50) = -115 and log(2.5*10^-15) = -33.6
The shameless self promotion got me lol, u're so funny
Thanks! BAM! :)
Hello! Does it matter if the data in one of the columns (say popcorn) is not normally distributed? Or should the assumption be that we will have a large enough sample size to use the central limit theorem?
Thanks for all of your videos! I love them and can’t wait for your book to be delivered (just ordered it yesterday).
It doesn't matter how the data are distributed. As long as we can calculate the likelihoods, we are good to go. BAM! :) And thank you so much for supporting StatQuest!!! TRIPLE BAM!!! :)
You always get the like after the intro song hahaha
Bam! Thank you very much! :)
dude you are awesome
Thank you!
Hi Josh.. Thank you very much for your tutorial video. I am a big fan sir
I have a clarification. The P(Love Troll) or P (No love Troll) given the 3 variables - here in this example - we multiply the Prior Probability of the class with the likelihood of the variables given the class ... However as per Bayes's Theorem, it is also divided by the probability (or likelihood) of the variables... which is not done in this tutorial, same with the Naive Bayes "clearly explained" tutorial... I am sorry if have asked something "naive" :)
You have hit on one of the reasons I do not mention bayes' theorem in either of these videos. These methods are called "naive bayes", but they only use the numerator of that equation, because calculating the denominator would be hard to do. That said, the denominator would be the same for both classes, so it scales all "scores" by the same amount. And since we are only interested in the highest relative score, we can omit the denominator and still get the job done.
@@statquest Thank You very much for the clarification
Hi Josh, I wanted to know as to how do we get the likelihood from the y axis ?? lets say in the video at 4:12 you get the likelihood from the y axis for drinks 500 ml of soda given the person loves troll 2 to be 0.004. So how are we getting 0.004 ?
That distribution has a mean = 500 and standard deviation = 100. So I plug those numbers, plus x = 500 into the equation for a normal distribution (see ruclips.net/video/Dn6b9fCIUpM/видео.html ) and the value that comes out is 0.004.
@@statquest Thanks a lot for clearing it up !!
thank you for ur service T.T
Thanku bam🔥🔥
:)
Thanks for this super clear explanation. Why would we prefer this method for classification over a gradient boosting algorithm? When we have too few samples?
With relatively small datasets it's simple and fast and super lightweight.