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.
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'm working on it. I have 5 videos so far, and 5 more to go before I have the whole playlist. Here's the link to the first one: ua-cam.com/video/CqOfi41LfDw/v-deo.html and the other links are here: statquest.org/video-index/
@@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!
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.
Thanks for the very informative info ! Though I have a question , if my dataset is filled with just categorical string data. So no numerical data . Is there a way I can still use knn to predict ? I heard about encoding the string to numerical value but that seems very complex with big dataset .
If you use R, then you can use a Random Forest to cluster anything and then apply KNN to that clustering: ua-cam.com/video/sQ870aTKqiM/v-deo.html If you don't use R, you can use target encoding: ua-cam.com/video/589nCGeWG1w/v-deo.html
Thank you very much for your amazing work! Question kind of not related, but I was wondering: is there any explanation on euclidean distance calculated in stata as well? Thanks!
That is awsom how you explain this topics. One suggestion, you could show how the 7 nearest ist red, 3 nearest ist orange and 1 nearest is green for the point in the middle. By my eyes, the 1 nearest neigbour ist still red! and it makes me confuse what does nearest means actually :)
Just come across the video! Love it!! It's really clear and easy to follow! :D I have a question regarding the steps. For step 1, you said it would be used for known categories, and Im looking to use this method for unknown categories. Since we know all most of the traits, is there anyway to create categories using those characteristics? I'm new for machine learning and I wonder is there any method for this?
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
Support StatQuest by buying my book The StatQuest Illustrated Guide to Machine Learning or a Study Guide or Merch!!! statquest.org/statquest-store/
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.
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. :)
INTRO IS LEGENDARY BRO : )
Yup, that's a good one. :)
When a random UA-cam channel explains it better than your University Professor....
Keep it up!
Wow, thanks!
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! :)
it is good to listen to your music in your website after watching this clear-explained video. thanks a lot.
Thank you so much! :)
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
This channel is salt of the Earth
Thanks!
When I search for something and find it on StatQuest channel. Super BAM!!
YES!
Thank you josh and the FFGDUNCCH (the friendly folks from the genetics department at the university of north carolina at chapel hill)
Triple bam! :)
I can't believe how good you are at explaining this. wow!!!
bam!
Thank you, very clear and to the point explanation !
Very clear, I got the idea of this concept right away.
Well done, thanks!
THanks!
Thank you so much. So useful honestly - i didnt get this from a 2 hour lecture
Glad it was helpful!
Thank you! This helped me so much in understanding KNN faster :D
Hooray!!! :)
This channel is GOD SENT. Period.
Thanks!
Amazing explanation! Thank you!
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! 💪🏼👊🏼
I am brushing up on my ML terminology and StatQuest always comes to the rescue!! BAM!
bam!
WOWW! This was super helpful!
Thanks Josh!
Glad it was helpful!
Thank you for your Clear explanation.
You're welcome! :)
Clear and concise explanation. Thank you :)
Thanks! :)
Thank you. Very good explanation in such a short time.
Thanks! :)
I am so glad I found this channel.
Thanks!
Very well explained and loved your uke intro by the way :)
Thank you!
Another exciting episode of statquest!
bam! :)
Easy to understand and straightforward. Thanks.
Thanks!
Simple and Clear explanation. Thank you!
Thanks!
Ohhh man this so simple
Thqqq for this type of explanation
Most welcome 😊
BAM!!! That was great as usual.
Hooray! Thank you! :)
such an amazing explanation. Thank you!
Thanks! :)
awesome explanation ! thank you so much!
Thank you! :)
You're a legend at explaining.
:)
BAM! Amazing explanation!
Thanks!
Loved it.... Thank you 😊
Glad you enjoyed it!
Bam! Smart and clear as usual.
Many thanks for the clear explanation
Thanks! :)
Thank you so much for saving our time sir❤ love from Srilanka 🇱🇰
bam!
Wow! such a great explainer
Glad you think so!
Great explanation! BAM! Great illustrations! Double BAM!!
Thank you very much! :)
Dang. Simple and to the point! Thank you!
Thanks!
I love you sir! Your video save my life!
Happy to help!
thank you so much.This was well explained.
Thanks!
That opening banjo solo is prettt sweet.
Thanks!
Good stuff, thanks! Do you have any videos about survival analysis?
Summarised in a very short video....just perfect
Thank you! :)
Best explanation ever, thank you!!!
Thanks!
Great tutorial!
Thank you!
Your videos are K-nearest perfection :)
Ha! Very funny.
@@statquest Noice 👍 Thanks 👍
Well explained, thank you good sir!
Glad it was helpful!
you are the master of machine learning
:)
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 🙂.
BAM!!! You nailed it.
Thank you! :)
You are amazing! Thank u so much.
Cheers from BRAZIL
Muito obrigado! :)
Thank you so much
No problem!
You are awesome man!!
Thanks!
BAM!
:)
THANK YOU JOSH!
Anytime! :)
You're a legend ! Thank you :)
Thanks!
lifesaver! thank you!
Glad it helped!
Thanks sir, great explanation!
Glad you liked it!
Thanks alot for this video.
Hooray! :)
Thank you!
You bet!
Good job ! I loved the videooo :)
Thanks!
Where would I be without StatQuest? Luckily, I now have the statistical tool to estimate this!
bam!
Omg, thank you so much!!!!!
Happy to help!
Nice video well done
Thanks!
was extremely helpful tysm
Thanks! :)
THANK YOU!
YOU HAVE SAVED ME :D
Awesome! :)
awesome! You should do a quadratic discriminant analysis to go with your awesome one on LDA
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.
one video explained better than a whole semester
Awesome! :)
Excellent
Thank you so much 😀
Omg thank you so much
No problem!
Amazing!
Thanks!
great video
Thanks!
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.
Thanks!
thanks a lot bro
Any time! :)
Hello Josh how are you. I was wondering if you may kindly explain the Naive Bayes, to be clearly explained :)
My 10 year old hums statquest song made me realise I my new obsession with this
bam!
thanks nice tutorial
Thank you! :)
I like your bandcamp!
Hooray! Thank you! :)
It is unfair that I can't give this video another like.
:)
Thanks for your youtube :)
No problem 😊
Your videos are really great! Clear and detailed explanation. Can you please make a similar detailed playlist for neural networks?
I'm working on it. I have 5 videos so far, and 5 more to go before I have the whole playlist. Here's the link to the first one: ua-cam.com/video/CqOfi41LfDw/v-deo.html and the other links are here: statquest.org/video-index/
@@statquest Yes I have seen those videos, just wanted to know whether there are more videos to come. Eagerly waiting!
@@unnatinandrekar99 The next one comes out on Monday, and then the rest will come out, one or two per week, for the next month.
@@statquest BAM!!!! That's prefect!!!!!!!!
please do statquest videos on complete model building projects in R!!
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.
that was exciting indeed
Hooray! :)
BAM subscribed.
Thank you!
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.
Thanks for the very informative info ! Though I have a question , if my dataset is filled with just categorical string data. So no numerical data . Is there a way I can still use knn to predict ? I heard about encoding the string to numerical value but that seems very complex with big dataset .
If you use R, then you can use a Random Forest to cluster anything and then apply KNN to that clustering: ua-cam.com/video/sQ870aTKqiM/v-deo.html If you don't use R, you can use target encoding: ua-cam.com/video/589nCGeWG1w/v-deo.html
Thank you very much for your amazing work! Question kind of not related, but I was wondering: is there any explanation on euclidean distance calculated in stata as well? Thanks!
Unfortunately I don't know how to use stata.
That is awsom how you explain this topics. One suggestion, you could show how the 7 nearest ist red, 3 nearest ist orange and 1 nearest is green for the point in the middle. By my eyes, the 1 nearest neigbour ist still red! and it makes me confuse what does nearest means actually :)
What time point, minutes and seconds, are you referring to?
awesome again
double :)
Just come across the video! Love it!! It's really clear and easy to follow! :D I have a question regarding the steps. For step 1, you said it would be used for known categories, and Im looking to use this method for unknown categories. Since we know all most of the traits, is there anyway to create categories using those characteristics? I'm new for machine learning and I wonder is there any method for this?
It depends on a lot of things. Creating categories from the raw data can be very subjective.
@@statquest Would it be possible to categorize items having trait 1,2,3,4 using similarity tests? But then the question is then where to start with.
thanks you
:)
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! :)