I literally understood this thing just by watching this video ONCE. I hope you realized how good is your ability to teach. Who ever have chance to be your student is blessed.
I love the way you explain math using real world scenarios. Its makes us easy to understand and grasp the concept very easily. Thank you sir your are a good teacher even 5 years old kid will understand this algorithm very easily by the way you explained.
I have just finished watching it. Fully understood. Sir right from the word go I have watched all your videos and all of them are simple and easy to comprehend. Keep posting... Waiting for the next...
I really appretiate the indian utubers who guides us with full force...because of this. I usually try to search the indian guiders for any topic relate to study.
Ooh my God! I just understood this now because of you after so many trials to read.... May God bless you man! You Indians are a blessing to this Universe 🎉
Yogesh Ji, I have seen your vlog for the first time and really impressed with the ease of understanding you are inculcating in the explanation. Thanks and keep sharing such useful vlogs.
Clearly explained with simple example.. within short span of time I cleared the concept.. but you have to include python programming for this algo also
Kudos to the explanation. Your efforts are appreciated. But there are some correction to be made. 1) The Euclidean distance of Zaira is 29 and not 9 2) The Euclidean distance of Smith and Michael with respect to Angelina are equal 3) The 3 nearest Euclidean distance with respect to Angelina are of Names = (Sara , Smith , Michael) Euclidean Distances = (11 , 10.049 , 10.049) Sport Class = (Cricket , Cricket , Football) Anyways decision will remain the same (i.e - Prediction will be that Angelina will belong to Cricket Class) But what if the sport class were (Cricket , Neither , Football). As we have 3 category in the sport class so it is not a binary classification what will be the conclusion in the above mentioned case?
@@abhishek0001. Can be given in the question but if that is not given there is always another way to start. Count the number of possibilities there are in class attribute you are trying to predict (in this case it's class of sport). There are 3 possible options there { neither, football, cricket } therefore you start with 3. Do not make k less than the number of class values, meaning in this situation you would not make k=2. One thing that can happen--If 3 is not giving you a definitive answer for which sport Angelina is most likely to play, you must raise the value of k. Nearest neighbors are the records with the lowest distance. For example k=3, and the 1st nearest neighbors sport = neither, 2nd nearest neighbor = cricket, 3rd nearest neighbor = football, this doesn't tell you which sport Angelina is most likely to play as there is no repeating sport. This is when you raise the value of k. With k=4 and 3 possible class of sport options 1 sport must be more common than the others.
Very clear. I'm not in this kind of things, but I think that it can be weighed in some ways. In this example it seems that the gender is not very important, because in the euclidean distance it doesn't really count compared to the age gap . If I want to give it more importance I can use male=0 and female=10 or male=0 and female=20.
Thank for video with such clear, step by step explanation! There is one error: The distance between ZAIRA and ANGELINA is 29, not 9. So, the 3 closest distances to ANGELINA are: MICHAEL (distance: 10,05) - Football SMITH (distance: 10,05) - Cricket SARA (distance: 11) - Cricket
Great video! Think Smith is 10.05 and from my understanding for distance based algorithms (such as kNN) it's best to standardized the data so that columns with larger ranges don't over impact results
this is such a simple way of teaching like really with a pen and paper , I dont get it people using useless animations and out of the context examples making videos more complex to understand . This is the best way of retaining information to the memory also makes it way more crisp to understand .
you are awsome only one doubt i have as you have take example for 2 features what if there are 3 or more then 3 then how we are going to calculate it. like what would be the formula
Yeah ,So we Have to take values 10,10.05,11 which is from Arun-> Cricket,Micheal -> FootBall,Sara->Cricket. Where 2 are Cricket ,1 football .Majority is Cricket,Ans is Cricket.
As the video is nice and easily understandable ,but it contains some mistake in calculation so kindly check it before uploading the video here bro/sir. And it too useful . Great representation👍👍
Thanks for the video. That was good. What if the KNN prediction was not a majority one? Like in the above example if 3 candidates say 'cricket', 'football' and 'Neither'. Which one to choose ?
NO ONE..NO ONE explains topics more clearly than an Indian youtuber with pen and paper.Amazing video😄
hahahahahahaha
Can't be more true 😂
what he said
I do not know why university lectures a so good at making these concepts very complicated. This video truly helped me. Thank you!
I literally understood this thing just by watching this video ONCE. I hope you realized how good is your ability to teach. Who ever have chance to be your student is blessed.
Thank you..I am glad it helped you..I will be uploading the videos for this algorithm and other algorithms with proper and simple datasets..
Most people find an easy way to explain KNN, but that you so much taking the effort and make it so simple. 🤩
Best use of 13min. Videos like this are just gem! 💎💎💎 I wish university faculties could teach you like this.
I love the way you explain math using real world scenarios. Its makes us easy to understand and grasp the concept very easily. Thank you sir your are a good teacher even 5 years old kid will understand this algorithm very easily by the way you explained.
Very Nice Explanation,Sir.Needs More This Type Of Tutorial From You.
sure thank you
I have just finished watching it. Fully understood. Sir right from the word go I have watched all your videos and all of them are simple and easy to comprehend. Keep posting... Waiting for the next...
Wonderfully explained no other resource required again to understand KNN
I really appretiate the indian utubers who guides us with full force...because of this. I usually try to search the indian guiders for any topic relate to study.
Sir, As an outsider I became real fond of Indian YT,
How they present & instruct the material! 😌😍
Ooh my God! I just understood this now because of you after so many trials to read....
May God bless you man! You Indians are a blessing to this Universe 🎉
Best explanation of KNN I have seen on TH-cam. Thank you!
very well done....I looked for dozens of videos on this topic but yours is the ultimate ...awesome ...pl upload more videos..thank you!!!!!
Really very much needed and it helped me a lot... Clear and crisp
À
Thank you so much dada. I`m taking preparation for the final exam and it`s too much helpful for me.
Dear Sir, It's very helpful. Kindly make more videos in same format. Please. It opens our brain in simple way
Really nice explanation, I saw this video after seeing few other videos,
Your video explained full process in very clean way.
Thank you so much
Watching ....30 Min before Semester Exam.......Thanks bro🤙🤙🤙
Your videos give very neat description of machine learning algorithms. Please make more videos
Easy to understand, this is by best video I've watch that explained it clearly with examples.
Yogesh Ji, I have seen your vlog for the first time and really impressed with the ease of understanding you are inculcating in the explanation. Thanks and keep sharing such useful vlogs.
I am glad if it helped you.....
Clearly explained with simple example.. within short span of time I cleared the concept.. but you have to include python programming for this algo also
sure i will do that...
thank you very much for the video! helped me pass my exam on big data 😎
Hey bro.... I searched so many blogs and information about knn ...but not even bit of understand ....your explanation is really soo cool and awesome
One such satisfied conceptual explanation which i come across, Thank you so much
Very Simple but best video to understand...😍😍Thanks
Kudos to the explanation. Your efforts are appreciated.
But there are some correction to be made.
1) The Euclidean distance of Zaira is 29 and not 9
2) The Euclidean distance of Smith and Michael with respect to Angelina are equal
3) The 3 nearest Euclidean distance with respect to Angelina are of
Names = (Sara , Smith , Michael)
Euclidean Distances = (11 , 10.049 , 10.049)
Sport Class = (Cricket , Cricket , Football)
Anyways decision will remain the same (i.e - Prediction will be that Angelina will belong to Cricket Class)
But what if the sport class were (Cricket , Neither , Football).
As we have 3 category in the sport class so it is not a binary classification what will be the conclusion in the above mentioned case?
finally found the comment that i was looking for
How to assume the value of k?
@@abhishek0001. that will be given in the question
if sport class were cricket,neither,football then i think we have to increase the k value
@@abhishek0001. Can be given in the question but if that is not given there is always another way to start. Count the number of possibilities there are in class attribute you are trying to predict (in this case it's class of sport). There are 3 possible options there { neither, football, cricket } therefore you start with 3.
Do not make k less than the number of class values, meaning in this situation you would not make k=2. One thing that can happen--If 3 is not giving you a definitive answer for which sport Angelina is most likely to play, you must raise the value of k.
Nearest neighbors are the records with the lowest distance. For example k=3, and the 1st nearest neighbors sport = neither, 2nd nearest neighbor = cricket, 3rd nearest neighbor = football, this doesn't tell you which sport Angelina is most likely to play as there is no repeating sport. This is when you raise the value of k. With k=4 and 3 possible class of sport options 1 sport must be more common than the others.
Thanks sir, your explanation is very very easy to understand
thank u sir
iam from Iraq and I depend on ur videos
Your explanation is fully understandable. Cleared all points.
खूप छान, सुरळीत आणि सोपं👌👌👌
I should really say, thank you so much so so much. You have explained this concept very deeply and very easily. ❤❤
Love you yogesh brother the way your explaining is awesome brother
Thank you very much it helps, from Bangladesh
Thanks..I am glad if it helped you!!
Sir really explained in simple way.Thank you 👏
Excellent....very useful
Great explanation..Thanks ..last minute exam prep students like here
Great video, with a very clear explanation. I understood very easily with it.
Very clear. I'm not in this kind of things, but I think that it can be weighed in some ways. In this example it seems that the gender is not very important, because in the euclidean distance it doesn't really count compared to the age gap . If I want to give it more importance I can use male=0 and female=10 or male=0 and female=20.
Thank for video with such clear, step by step explanation!
There is one error: The distance between ZAIRA and ANGELINA is 29, not 9. So, the 3 closest distances to ANGELINA are:
MICHAEL (distance: 10,05) - Football
SMITH (distance: 10,05) - Cricket
SARA (distance: 11) - Cricket
Sir, there is a small mistake in the distance value of zaira, it’s 29 not 9. But rest of the video is excellent. Good explanation Sir.
Yes u r right.
Yes that's there ..but hope you got the concept...I will try to create a video with correct values.. thanks
@@yogeshmurumkar plz post it asap
Thank you so much, finally a simple clearly video about it
Well done Yogesh. Keep it up.
Great video! Think Smith is 10.05 and from my understanding for distance based algorithms (such as kNN) it's best to standardized the data so that columns with larger ranges don't over impact results
Yes that can be done during implementation!!
Bro why same details have small variation in distance?
You saved my life I have exam tomorrow !!!
After my college and other ytb'rs confused me as hell ..... you showed up as an angel.
this is such a simple way of teaching like really with a pen and paper , I dont get it people using useless animations and out of the context examples making videos more complex to understand . This is the best way of retaining information to the memory also makes it way more crisp to understand .
Superb
Thank you so much 🙏💫
This is brilliant. Thank you so much for the clear and simple explanation.
excellent. Clearest explanation so far
Explanation should be this simple.. You now have a new subscriber
Explained flawlessly ❤
Great video... It focussed on the math which exactly I was looking for
Sir, how to arrange in ascending order if two or more data points have the same Euclidian distances but have different classes.
Well done my friend, this really helped!
Great video, with clear explanation. Thank you sir.
Thank you bro, I will suscribe
That's why I love Indian people, thank you
Saved our sinking grades. Thank you
You explained very well. Thank you ❤
you are awsome only one doubt i have as you have take example for 2 features what if there are 3 or more then 3 then how we are going to calculate it. like what would be the formula
f
√(34-5)2 = 29 but u told it's near to value of 10 and gave prediction they like cricket abruptly in video. There's error
Its 29 n he wrote 9
Yeah ,So we Have to take values 10,10.05,11
which is from Arun-> Cricket,Micheal -> FootBall,Sara->Cricket. Where 2 are Cricket ,1 football .Majority is Cricket,Ans is Cricket.
@@pankajprajapati3972 yes
Exactly i was like with age 34 how it beats sara with 16 . he should correct it.
@@saiakhileshveldi4580 where is Arun in the example?
Thanks yogesh this was very helpful for me :)
As the video is nice and easily understandable ,but it contains some mistake in calculation so kindly check it before uploading the video here bro/sir. And it too useful . Great representation👍👍
Very simple n neat explanation of distance calculation
Really nice explanation...!!!
Thanks for sharing...!!!
Excellent!!!! you nailed it. Thanks for such a great explanation. I tried other videos as well but I believe yours was the best for the topic.
you have explained in an Efficient way: real world example with clear sights
7:45 very well explained
Very well explained. Helped me a lot. Thanks
Very beautiful way to explain sir
Man.....ur awesome... crystal clear
Best example on youtube !
Excellent
thanks for the very good explanation
Excellent explanation 👍👏
Thankyou very much , this was very helpful video
Its her choice 😂😂😂*confusing ML reactions*
HAHAHAAHA ur joke is so underrated man !! hilarious
lul 🤣
just like ABCD simple to understand 😊 I really needed this
Thanks for the video. That was good. What if the KNN prediction was not a majority one? Like in the above example if 3 candidates say 'cricket', 'football' and 'Neither'. Which one to choose ?
then increase your K value
Topic is cleared as crystal clear thanks a lot sir💕❣🤍
Very cool!! Just a little mistake... the distance between Angelina and Zaira is not 9 but 29
Yes you are right
Awesome, crystal clear friend
Great Explaining the Concept Sir, But I think Sara is needed to be considered in stead of Zaira as distance from Zaira is 29, not 9.
You are too good, thanks for the amazing video.
Clearly understand the concept thank you😊
Thanks A lot from Bangladesh
Wow.. Simple and clear👍
nice explanation bro
thank you, i didnt listen in class but you made it super clear
Thank you! This was very helpful.
i am glad if it helped you!!!
That's a nice explanation but I think you miscalculated the value of distance zaira, it will be 29
Superb explaination...wow... excellent
thank you for such a wonderful explaination
Will this be ideal for user interests ?
Thank u so much.. Well explained..
God bless you ... keep it up...
Thanks for great explanation
what if Angelina belongs to 3 different categories? what should we choose?