00:10 K-nearest neighbors is a simple algorithm for classifying data. 00:50 Clustering data using PCA and classifying new cell type 01:29 K-nearest neighbors classifies new data based on nearest annotated cells. 02:12 K-nearest neighbors algorithm assigns a category based on the majority of nearest neighbors' votes. 02:59 K-nearest neighbors algorithm classifies unknown points based on nearest neighbors 03:40 K-nearest neighbors can avoid ties by using an odd K value. 04:22 Choosing the best value for K is crucial for K-nearest neighbors. 05:01 Categories with few samples are outvoted
@@statquest It went well, thank you! Hopefully I get good grades. I was thinking of suggesting that it would be great if you could cover Markov Chain Monte Carlo and related topics. Thank you again! Your channel has been incredibly helpful!
These videos are just amazing and clearly are extremely successful in simplifying topics that are usually thought of as difficult. Can you please also make videos on its code in python/R..? and of naive bayes too maybe. That would be super useful. Thank you very much for this level of awesome content.
Just wow thanks Josh. You are just great. One doubt however, if k values are large will outliers not affect my algo? Effect of outliers in knn? Please answer.
When we have categorical variable like Yes/No or type of job (which can take four values: business, healthcare, engineering, or education), how can we calculate distances? Is knn useful at all?
If there is a distance metric, then it KNN will work, and there are distance metrics for categorical variables. See stackoverflow.com/questions/2981743/ways-to-calculate-similarity/2983763#2983763
I love this guy's shtick. Corny, slightly annoying music, although I'm sure he is a great musician. Slightly condescending voice when he goes over the material... like "I'm making this so fucking easy for you... you can't possibly not understand this". It's actually quite calming. He speaks slowly too. You don't have to constantly pause his videos. I understand everyone of his videos. If I don't, it's because I didn't yet watch any prerequisite videos that he tells you at the beginning to watch. He never takes for granted that you understand some detail. This is the BIGGEST freakin' mistake of educators. Some damn variable in a formula that they forget to explain. Also, he will use the simplest example possible so that you understand. I am returning to school, grad school in the ML track for computer science. I don't remember much of the math that I took 20 years ago. This guy is a lifesaver. Wish I watched these when I started. I will be watching all of his videos. After I graduate and make some money, I'm sending him some bucks thru Patreon. Thanks man!
BAM! Thank you very much! I think I must have "resting condescending voice" - because several people have made the comment that I sound a little condescending - but trust me this is not intentional! :)
@@statquest It's actually reassuring. You know, when you are talking to someone who is freaking out? And you make it sound like "Dood, this not that hard."
Your video is amazing as always... It would be great if you can include how to choose the value for 'k' and evaluation metrics for kNN. Also, if I understand it right, there is no actual "training" happening in kNN. It is about arranging the points on the cartesian plane and when a new data point comes, it will again be placed on the same plane and depending on the value of "k", it will be classified. Correct me if I'm wrong.
Hi. Yes, you are right. KNN is easy to implement and understand and has been widely used in academia and industry for decades. You may utilise the cross-validation technique and the validation datasets to select the value for k.
Hi . Thanks for video . About the concept of KNN , how the location of unknown cell change in scatter plot . You must change the location of that? And second question, we should change the value of k to reach best k ?
The location of the "unknown cell" is fixed - it does not change. Just the classification changes. I offer a few thoughts about how to pick 'k' at 4:12, but, other than that, you can use cross validation: th-cam.com/video/fSytzGwwBVw/w-d-xo.html
Support StatQuest by buying my book The StatQuest Illustrated Guide to Machine Learning or a Study Guide or Merch!!! statquest.org/statquest-store/
Whenever I search for a video tutorial, and you pop up in the search results, my heart fills with joy!!! ^^
Thank you once again!
Hooray!!!!! :)
same here ..not started the video yet but only 1 video on knn .....dont know if i can understand very very well like linear regression
I'm taking a machine learning course at university, and I've been blessed with having found your channel. Keep up the great content!
Hooray! I'm glad the videos are helpful. :)
Five minutes explains better than some teachers spent one hour. :)
Thank you! :)
Better than teacher spending semester for me
hahahahaha
@@free_thinker4958 wtf really? also my teacher took 5 minutes that's why I understood nothing
For real, this channel is a godsend.
INTRO IS LEGENDARY BRO : )
Yup, that's a good one. :)
Every time I see your videos I'm simply amazed how you manage to make things simple,it's like 1+1=2, respect
Thank you! :)
When a random TH-cam channel explains it better than your University Professor....
Keep it up!
Wow, thanks!
This channel is salt of the Earth
Thanks!
This channel is GOD SENT. Period.
Thanks!
This is by far the best video on KNN algo ! Thanks Josh
You are doing awesome work Sir..have watched your other videos as well..very intuitive and logically explained
it is good to listen to your music in your website after watching this clear-explained video. thanks a lot.
Thank you so much! :)
I am brushing up on my ML terminology and StatQuest always comes to the rescue!! BAM!
bam!
I can't believe how good you are at explaining this. wow!!!
bam!
Thank you josh and the FFGDUNCCH (the friendly folks from the genetics department at the university of north carolina at chapel hill)
Triple bam! :)
When I search for something and find it on StatQuest channel. Super BAM!!
YES!
Man, you are a legend, if I pass from the exam on Monday (which I am pretty hopeless), I will buy one of your shirts next month
Hooray! Good luck with your exam! :)
@@statquest Hey, I failed :D but still, I learnt a lot, thanks!
@@eltajbabazade1189 Better luck next time! :)
@@eltajbabazade1189 I hope you graduated successfully 🙂.
Thank you so much. So useful honestly - i didnt get this from a 2 hour lecture
Glad it was helpful!
00:10 K-nearest neighbors is a simple algorithm for classifying data.
00:50 Clustering data using PCA and classifying new cell type
01:29 K-nearest neighbors classifies new data based on nearest annotated cells.
02:12 K-nearest neighbors algorithm assigns a category based on the majority of nearest neighbors' votes.
02:59 K-nearest neighbors algorithm classifies unknown points based on nearest neighbors
03:40 K-nearest neighbors can avoid ties by using an odd K value.
04:22 Choosing the best value for K is crucial for K-nearest neighbors.
05:01 Categories with few samples are outvoted
You forgot the bam! :)
Your videos are K-nearest perfection :)
Ha! Very funny.
@@statquest Noice 👍 Thanks 👍
Thank you so much for saving our time sir❤ love from Srilanka 🇱🇰
bam!
Hey Josh! This is just a thank you note saying if I pass the upcoming exam, then it would be all because of you! ❤
Good luck!!! Let me know how it goes!
@@statquest It went well, thank you! Hopefully I get good grades. I was thinking of suggesting that it would be great if you could cover Markov Chain Monte Carlo and related topics. Thank you again! Your channel has been incredibly helpful!
@@suparnaroy2829 I'm glad it went well! And I'll keep those topics in mind.
you are the master of machine learning
:)
I am so glad I found this channel.
Thanks!
Your videos are sooo great, I can't stop watching 💖💖 thank you
Hooray!!!!
StatQuest with Josh Starmer can you add an ICA as well?
It's on the to-do list, but it might be a while before I get to it.
StatQuest with Josh Starmer 😔😕 that's sad, but i look forward to it. You explain beautifully sir! 💪🏼👊🏼
Simple and Clear explanation. Thank you!
Thanks!
one video explained better than a whole semester
Awesome! :)
You're a legend at explaining.
:)
Very clear, I got the idea of this concept right away.
Well done, thanks!
THanks!
WOWW! This was super helpful!
Thanks Josh!
Glad it was helpful!
Easy to understand and straightforward. Thanks.
Thanks!
Another exciting episode of statquest!
bam! :)
It is unfair that I can't give this video another like.
:)
Thank you for your Clear explanation.
You're welcome! :)
Very well explained and loved your uke intro by the way :)
Thank you!
Where would I be without StatQuest? Luckily, I now have the statistical tool to estimate this!
bam!
That opening banjo solo is prettt sweet.
Thanks!
Summarised in a very short video....just perfect
Thank you! :)
Thank you! This helped me so much in understanding KNN faster :D
Hooray!!! :)
Clear and concise explanation. Thank you :)
Thanks! :)
Ohhh man this so simple
Thqqq for this type of explanation
Most welcome 😊
Thank you, very clear and to the point explanation !
Dang. Simple and to the point! Thank you!
Thanks!
Thank you. Very good explanation in such a short time.
Thanks! :)
BAM! Amazing explanation!
Thanks!
Great explanation! BAM! Great illustrations! Double BAM!!
Thank you very much! :)
I love you sir! Your video save my life!
Happy to help!
You are amazing! Thank u so much.
Cheers from BRAZIL
Muito obrigado! :)
Many thanks for the clear explanation
Thanks! :)
Best explanation ever, thank you!!!
Thanks!
Amazing explanation! Thank you!
awesome explanation ! thank you so much!
Thank you! :)
thank you so much.This was well explained.
Thanks!
THANK YOU JOSH!
Anytime! :)
Great video man
Thanks!
BAM!!! That was great as usual.
Hooray! Thank you! :)
You're a legend ! Thank you :)
Thanks!
BAM!
:)
Wow! such a great explainer
Glad you think so!
These videos are just amazing and clearly are extremely successful in simplifying topics that are usually thought of as difficult. Can you please also make videos on its code in python/R..? and of naive bayes too maybe. That would be super useful. Thank you very much for this level of awesome content.
I'll keep that in mind.
Loved it.... Thank you 😊
Glad you enjoyed it!
Thanks sir, great explanation!
Glad you liked it!
Thank you so much
No problem!
lifesaver! thank you!
Glad it helped!
You are awesome man!!
Thanks!
Great tutorial!
Thank you!
Well explained, thank you good sir!
Glad it was helpful!
BAM!!! You nailed it.
Thank you! :)
Thank you!
You bet!
Bam! Smart and clear as usual.
Thanks, you're great
Thanks!
So much clearer than my lecturer fam
Thanks!
@@statquest no, thank you :)
THANK YOU!
YOU HAVE SAVED ME :D
Awesome! :)
Good job ! I loved the videooo :)
Thanks!
I like your bandcamp!
Hooray! Thank you! :)
Thanks alot for this video.
Hooray! :)
My 10 year old hums statquest song made me realise I my new obsession with this
bam!
Omg thank you so much
No problem!
Omg, thank you so much!!!!!
Happy to help!
Nice video well done
Thanks!
Josh was in Bangaluru, I saw him there!!
bam! :)
Just wow thanks Josh. You are just great. One doubt however, if k values are large will outliers not affect my algo? Effect of outliers in knn? Please answer.
I believe that large values for K will provide some protection from outliers.
I liked the video immediately after hearing the guitar intro
bam! :)
Excellent
Thank you so much 😀
It was super simple indeed!
:)
awesome! You should do a quadratic discriminant analysis to go with your awesome one on LDA
When we have categorical variable like Yes/No or type of job (which can take four values: business, healthcare, engineering, or education), how can we calculate distances? Is knn useful at all?
If there is a distance metric, then it KNN will work, and there are distance metrics for categorical variables. See stackoverflow.com/questions/2981743/ways-to-calculate-similarity/2983763#2983763
thanks a lot bro
Any time! :)
was extremely helpful tysm
Thanks! :)
Hail Joshua!!
BAM! :)
Amazing!
Thanks!
I love this guy's shtick. Corny, slightly annoying music, although I'm sure he is a great musician. Slightly condescending voice when he goes over the material... like "I'm making this so fucking easy for you... you can't possibly not understand this". It's actually quite calming. He speaks slowly too. You don't have to constantly pause his videos. I understand everyone of his videos. If I don't, it's because I didn't yet watch any prerequisite videos that he tells you at the beginning to watch.
He never takes for granted that you understand some detail. This is the BIGGEST freakin' mistake of educators. Some damn variable in a formula that they forget to explain. Also, he will use the simplest example possible so that you understand.
I am returning to school, grad school in the ML track for computer science. I don't remember much of the math that I took 20 years ago. This guy is a lifesaver. Wish I watched these when I started. I will be watching all of his videos.
After I graduate and make some money, I'm sending him some bucks thru Patreon.
Thanks man!
BAM! Thank you very much! I think I must have "resting condescending voice" - because several people have made the comment that I sound a little condescending - but trust me this is not intentional! :)
@@statquest It's actually reassuring. You know, when you are talking to someone who is freaking out? And you make it sound like "Dood, this not that hard."
@@Steve-3P0 Nice! :)
Good stuff, thanks! Do you have any videos about survival analysis?
great video
Thanks!
Thanks for your youtube :)
No problem 😊
You are Legend
Thanks!
BAM subscribed.
Thank you!
that was exciting indeed
Hooray! :)
Your video is amazing as always... It would be great if you can include how to choose the value for 'k' and evaluation metrics for kNN. Also, if I understand it right, there is no actual "training" happening in kNN. It is about arranging the points on the cartesian plane and when a new data point comes, it will again be placed on the same plane and depending on the value of "k", it will be classified. Correct me if I'm wrong.
Hi. Yes, you are right. KNN is easy to implement and understand and has been widely used in academia and industry for decades. You may utilise the cross-validation technique and the validation datasets to select the value for k.
Hi . Thanks for video . About the concept of KNN , how the location of unknown cell change in scatter plot . You must change the location of that? And second question, we should change the value of k to reach best k ?
The location of the "unknown cell" is fixed - it does not change. Just the classification changes. I offer a few thoughts about how to pick 'k' at 4:12, but, other than that, you can use cross validation: th-cam.com/video/fSytzGwwBVw/w-d-xo.html
StatQuest with Josh Starmer I got it . Thank you 🙏 😀
watch for the stats, stay for the intro songs
bam! :)